Genes involved estrogen metabolism

ABSTRACT

The invention concerns genes that have been identified as being involved in estrogen metabolism, and are useful as diagnostic, prognostic and/or predictive markers in cancer. In particular, the invention concerns genes the tumor expression levels of which are useful in the diagnosis of cancers associated with estrogen metabolism, and/or in the prognosis of clinical outcome and/or prediction of drug response of such cancers.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a non-provisional application filed under 37 C.F.R. §1.53(b), claiming priority under 37 C.F.R. §119(e) to U.S. Provisional Patent Application Ser. No. 60/787,926, filed on Mar. 31, 2006 and to U.S. Provisional Patent Application Ser. No. 60/789,187, filed on Apr. 3, 2006, the entire disclosures of which are hereby expressly incorporated by reference.

FIELD OF THE INVENTION

The present invention concerns genes that have been identified as being involved in estrogen metabolism, and are useful as diagnostic, prognostic and/or predictive markers in cancer. In particular, the present invention concerns genes the tumor expression levels of which are useful in the diagnosis of cancers associated with estrogen metabolism, and/or in the prognosis of clinical outcome and/or prediction of drug response of such cancers.

DESCRIPTION OF THE RELATED ART

Gene Expression Studies

Oncologists regularly confront treatment decisions regarding whether a cancer patient should receive treatment and, if so, what treatment to choose. These oncologists typically have a number of treatment options available to them, including different combinations of chemotherapeutic drugs that are characterized as “standard of care.” Because these “standard of care” chemotherapeutic drugs such as cyclophosphamide, methotrexate, 5-fluorouracil, anthracyclines, taxanes, have limited efficacy and a spectrum of often severe side effects, it is important to identify those patients having the highest likelihood of a positive clinical outcome without chemotherapy (patients with good prognosis) in order to minimize unnecessary exposure of these patients to the toxic side effects of the chemotherapeutic agents.

For those patients with a poor prognosis it is then important to predict the likelihood of beneficial response in individual patients to particular chemotherapeutic drug regimens. Identification of those patients most likely to benefit from each available treatment will enhance the utility of “standard of care” treatments, and facilitate the development of further, more personalized treatment options, including the use of already approved drugs that had previously not been recommended for the treatment of a particular cancer. The identification of patients who are more likely or less likely to need and respond to available drugs thus could increase the net benefit these drugs have to offer and decrease net morbidity and toxicity, via more intelligent patient selection.

Most diagnostic tests currently used in clinical practice are single analyte, and therefore do not capture the potential value of knowing relationships between dozens of different markers. Moreover, diagnostic tests are often based on immunohistochemistry, which is not quantitative. Immunohistochemistry often yields different results in different laboratories, in part because the reagents are not standardized, and in part because the interpretations are subjective. RNA-based tests, while potentially highly quantitative, have not been used because of the perception that RNA is destroyed in tumor specimens as routinely prepared, namely fixed in formalin and embedded in paraffin (FPE), and because it is inconvenient to obtain and store fresh tissue samples from patients for analysis.

Over the last two decades molecular biology and biochemistry have revealed hundreds of genes whose activities influence the behavior of tumor cells, their state of differentiation, and their sensitivity or resistance to certain therapeutic drugs. However, with a few exceptions, the status of these genes has not been exploited for the purpose of routinely making clinical decisions about drug treatments. In the last few years, several groups have published studies concerning the classification of various cancer types by microarray gene expression analysis of thousands of genes (see, e.g. Golub. et al., Science 286:531-537 (1999); Bhattacharjae et al., Proc. Natl. Acad. Sci. USA 98:13790-13795 (2001); Chen-Hsiang et al., Bioinformatics 17 (Suppl. 1):S316-S322 (2001); Ramaswamy et al., Proc. Natl. Acad. Sci. USA 98:15149-15154 (2001); Martin et al., Cancer Res. 60:2232-2238 (2000); West et al., Proc. Natl. Acad. Sci. USA 98:11462-114 (2001); Sorlie et al., Proc. Natl. Acad. Sci. USA 98:10869-10874 (2001); Yan et al., Cancer Res. 61:8375-8380 (2001)). However, these studies have not yet yielded tests routinely used in clinical practice, in large part because microarrays require fresh or frozen tissue RNA and such specimens are not present in sufficient quantity to permit clinical validation of identified molecular signatures.

In the past three years, it has become possible to profile gene expression of hundreds of genes in formalin-fixed paraffin-embedded (FPE) tissue using RT-PCR technology. Methods have been described that are highly sensitive, precise, and reproducible (Cronin et al., Am. J. Pathol. 164:35-42 (2004); PCT Publication No. WO 2003/078,662; WO 2004/071,572; WO 2004/074,518; WO 2004/065,583; WO 2004/111,273; WO 2004/111,603; WO 2005/008,213; WO 2005/040,396; WO 2005/039,382; WO 2005/064,019, the entire disclosures of which are hereby expressly incorporated by reference). Because thousands of archived FPE clinical tissue specimens exist with associated clinical records, such as survival, drug treatment history, etc., the ability to now quantitatively assay gene expression in this type of tissue enables rapid clinical studies relating expression of certain genes to patient prognosis and likelihood of response to treatments. Using data generated by past clinical studies allows for rapid results because the clinical events are historical. In contrast, for example, if one wished to carry out a survival study on newly recruited cancer patients one would generally need to wait for many years for statistically sufficient numbers of deaths to have occurred.

Breast Cancer Prognosis and Prediction

Breast cancer is the most common type of cancer among women in the United States, and is the leading cause of cancer deaths among women between the ages of 40 and 59.

Because current tests for prognosis and for prediction of chemotherapy response are inadequate, breast cancer treatment strategies vary between oncologists (Schott and Hayes, J. Clin. Oncol. PMID 15505274 (2004); Hayes, Breast 12:543-9 (2003)). The etiology of certain types of human breast cancer involves certain steroid hormones, called estrogens. Estrogens are believed to cause proliferation of breast epithelial cells primarily via binding of hormones to estrogen receptors, resulting in modification of the cellular transcription program. For these reasons, one of the most commonly used markers in selecting a treatment option for breast cancer patients is the estrogen receptor 1 (ESR1). Estrogen receptor-positive (ESR1+) tumors are generally less aggressive than estrogen receptor negative (ESR1−) tumors, and can often be successfully treated with anti-estrogens such as tamoxifen (TAM). Conversely, ESR1− tumors are typically more aggressive and are resistant to anti-estrogen treatment. Thus, aggressive chemotherapy is often provided to patients for ESR1− tumors. Based on this simple understanding, assays for ESR1 levels by immunohistochemistry are currently utilized as one parameter for making treatment decisions in breast cancer. Generally, lymph node negative patients whose tumors are found to be ESR1 positive are treated with an anti-estrogen drug, such as tamoxifen (TAM), and patients whose tumors are found to be ESR1 negative are treated with chemotherapy. However, often because of the uncertainty in the currently used diagnostic procedures, ESR1 positive patients are also prescribed chemotherapy in addition to anti-estrogen therapy, accepting the toxic side effects of chemotherapy in order to modestly decrease the risk of cancer recurrence. Toxicities include, neuropathy, nausea and other gastrointestinal symptoms, hair loss and cognitive impairment. Recurrence is to be feared because recurrent breast cancer is usually metastatic and poorly responsive to treatment.

The human GSTM (GSTμ) gene family consists of five different closely related isotypes, GSTM1 -GSTM5. GSTM proteins conjugate glutathione to various electrophilic small molecules, facilitating clearance of the electrophiles from cells. Evidence exists that several metabolites of estrogen, including estrogen semi-quinones and estrogen quinones (catechol estrogens), are toxic and mutagenic (Cavalieri et al., Proc Natl Acad Sci 94:10937-42, 1997). The activity of one or more GSTM enzymes may limit mutational damage caused by these estrogen metabolites.

We have reported five independent clinical studies in which GSTM gene expression was examined by quantitative RT-PCR in formalin-fixed, paraffin embedded primary breast cancer tissues. GSTM expression correlated strongly with favorable clinical outcome in each of these studies (Esteban et al., Prog. Proc Am Soc. Clin. Oncol. 22:850 abstract, 2003; Cobleigh et al., Clin Cancer Res (in press); Paik et al., Breast Cancer Res. Treat. 82:A16 abstract, 2003; Habel et al, Breast. Cancer Res. Treat. 88:3019 abstract, 2004: Paik et al, N Engl J Med 351:2817-26, 2004).

In these studies the probe used could not discriminate between GSTM1 and several other GSTM family members as a result of the strong sequence similarity of the GSTM genes, amplicon size limitations and the stringent sequence criteria for probe-primer design, leaving the possibility that several of the GSTM genes may be favorable markers.

Clearly, a need exists to identify those patients who are at substantial risk of cancer recurrence (i.e., to provide prognostic information) and/or likely to respond to chemotherapy (i.e., to provide predictive information). Likewise, a need exists to identify those patients who do not have a significant risk of recurrence, and/or who are unlikely to respond to chemotherapy, as these patients should be spared needless exposure to these toxic drugs.

SUMMARY OF THE INVENTION

The present invention is based, at least in part, on the recognition that since estrogens may contribute to tumorigenesis and tumor progression via pathways that are ESR1 independent, treatment decisions based primarily or solely on the ESR1 status of a patient are unsatisfactory.

One aspect of the invention is directed to a method of predicting clinical outcome for a subject diagnosed with cancer, comprising determining evidence of the expression level of one or more predictive RNA transcripts listed in Table 8, or their expression products, in a biological sample comprising cancer cells obtained from said subject, wherein evidence of increased expression of one or more of the genes listed in Table 8, or the corresponding expression product, indicates a decreased likelihood of a positive clinical outcome. In one embodiment the subject is a human patient. In one embodiment the expression level is obtained by a method of gene expression profiling. In one embodiment the method of gene expression profiling is a PCR-based method. In one embodiment the expression levels are normalized relative to the expression levels of one or more reference genes, or their expression products. In one embodiment the clinical outcome is expressed in terms of Recurrence-Free Interval (RFI), Overall Survival (OS), Disease-Free Survival (DFS), or Distant Recurrence-Free Interval (DRFI). In one embodiment the cancer is selected from the group consisting of breast cancer or ovarian cancer. In one embodiment the cancer is breast cancer.

In one embodiment, the method of predicting clinical outcome for a subject diagnosed with cancer comprises determining evidence of the expression level of at least two of said genes, or their expression products. In another embodiment, the expression levels of at least three of said genes, or their expression products are determined. In yet another embodiment, the expression levels of at least four of said genes, or their expression products are determined. In a further embodiment, the expression levels of at least five of said genes, or their expression products are determined.

The method may further comprise the step of creating a report summarizing said prediction.

Another aspect of the invention is a method of predicting the duration of Recurrence-Free Interval (RFI) in a subject diagnosed with breast cancer, comprising determining the expression level of one or more predictive RNA transcripts listed in Table 8 or their expression products, in a biological sample comprising cancer cells obtained from said subject, wherein evidence of increased expression of one or more of the genes listed in Table 8, or the corresponding expression product, indicates that said RFI is predicted to be shorter. In one embodiment the subject is a human patient. In another aspect the expression level is obtained by a method of gene expression profiling. In one embodiment the method of gene expression profiling is a PCR-based method. In one embodiment the expression levels are normalized relative to the expression levels of one or more reference genes, or their expression products. In one embodiment the clinical outcome is expressed in terms of Recurrence-Free Interval (RFI), Overall Survival (OS), Disease-Free Survival (DFS), or Distant Recurrence-Free Interval (DRFI). In one embodiment the cancer is selected from the group consisting of breast cancer or ovarian cancer. In one embodiment the cancer is breast cancer.

One aspect of the method of predicting the duration of Recurrence-Free Interval (RFI) for a subject diagnosed with cancer, comprises determining evidence of the expression level of at least two of said genes, or their expression products. In one embodiment the expression levels of at least three of said genes, or their expression products are determined. In another embodiment the expression levels of at least four of said genes, or their expression products are determined. In another embodiment the expression levels of at least five of said genes, or their expression products are determined.

One aspect of the methods of this invention is that if the RFI is predicted to be shorter, said patient is subjected to further therapy following surgical removal of the cancer. In one aspect, the therapy is chemotherapy and/or radiation therapy.

One aspect of the methods of this invention is that the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of CAT, CRYZ, CYP4Z1, CYP17A1, GPX1, GPX2, GSTM1, GSTM2, GSTM3, GSTM4, GSTM5, GSTP1, NQO1, PRDX3, and SC5DL is determined.

One aspect of the methods of this invention is that the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of GSTM1, GSTM2, GSTM3, GSTM4, GSTM5 and GSTP1 is determined.

One aspect of the methods of this invention is that the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of GSTM2 and GSTM4 is determined.

One aspect of the methods of this invention is that the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of GSTM1 and GSTM3 is determined.

One aspect of the methods of this invention is that the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of CAT, PRDX3, GPX1, and GPX2 is determined.

One aspect of the methods of this invention is that the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of PRDX3, GPX1 and GPX2 is determined.

One aspect of the methods of this invention is that the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of GPX1 and GPX2 is determined.

One aspect of the methods of this invention is that the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of CRYZ and NQO 1 is determined.

One aspect of the methods of this invention is that the expression level of one or more predictive RNA transcripts or their expression products of CYP17A1 is determined.

One aspect of the methods of this invention is that the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of SC5DL and CYP4Z1 is determined.

In another aspect, this invention concerns a method for preparing a personalized genomics profile for a patient comprising the steps of

-   -   (a) subjecting RNA extracted from a tissue obtained from the         patient to gene expression analysis;     -   (b) determining the expression level in the tissue of one or         more genes selected from the gene set listed in Table 8 ,         wherein the expression level is normalized against a control         gene or genes and optionally is compared to the amount found in         a cancer reference set and     -   (c) creating a report summarizing the data obtained by said gene         expression analysis.

Another embodiment of this invention is a method for amplification of a gene listed in Table 8 by polymerase chain reaction (PCR) comprising performing said per by using amplicons listed in Table 7 and a primer-probe set listed in Table 6.

Another embodiment of this invention is a PCR primer-probe set listed in Table 6.

Another embodiment of this invention is a PCR amplicon listed in Table 7.

BRIEF DESCRIPTION OF THE FIGURES AND TABLES

FIG. 1 shows the sequence alignment of the GSTM1 and GSTM2 amplicons with the corresponding regions of other GSTM family members.

FIG. 2 shows the distribution of RT-PCR signals as CT values (X-axis) across the 125 breast cancer patients (Y-axis) for GSTM1.1, GSTM1int5.2 and GSTM2int4.2.

FIG. 3 shows the distribution of RT-PCR signals as CT values for 22 human subjects for the different GSTM amplicons.

FIG. 4 shows the similarity and chromosome location of the GSTM genes.

FIG. 5 shows the cellular pathways which are the possible basis for the correlation of GSTM expression with good outcome.

FIG. 6 shows specific pathways for the degradation, modification and clearance of key estrogens, estrone and estradiol.

FIG. 7 shows specific pathways for the synthesis of key estrogens, estrone and estradiol, from cholesterol.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

A. Definitions

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed.,. J. Wiley & Sons (New York, N.Y. 1994); and Webster's New World™ Medical Dictionary, 2nd Edition, Wiley Publishing Inc., 2003, provide one skilled in the art with a general guide to many of the terms used in the present application. For purposes of the present invention, the following terms are defined below.

The term RT-PCR has been variously used in the art to mean reverse-transcription PCR (which refers to the use of PCR to amplify mRNA by first converting mRNA to double stranded cDNA) or real-time PCR (which refers to ongoing monitoring in ‘real-time’ of the amount of PCR product in order to quantify the amount of PCR target sequence initially present. The term “RT-PCR’ means reverse transcription PCR. The term quantitative RT-PCR (qRT-PCR) means real-time PCR applied to determine the amount of mRNA initially present in a sample.

The term “clinical outcome” means any measure of patient status including those measures ordinarily used in the art, such as disease recurrence, tumor metastasis, overall survival, progression-free survival, recurrence-free survival, and distant recurrence-free survival. Distant recurrence-free survival (DRFS) refers to the time (in years) from surgery to the first distant recurrence.

The term “microarray” refers to an ordered arrangement of hybridizable array elements, preferably polynucleotide probes, on a substrate.

The term “polynucleotide,” when used in singular or plural, generally refers to any polyribonucleotide or polydeoxribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA. Thus, for instance, polynucleotides as defined herein include, without limitation, single- and double-stranded DNA, DNA including single- and double-stranded regions, single- and double-stranded RNA, and RNA including single- and double-stranded regions, hybrid molecules comprising DNA and RNA that may be single-stranded or, more typically, double-stranded or include single- and double-stranded regions. In addition, the term “polynucleotide” as used herein refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The strands in such regions may be from the same molecule or from different molecules. The regions may include all of one or more of the molecules, but more typically involve only a region of some of the molecules. One of the molecules of a triple-helical region often is an oligonucleotide. The term “polynucleotide” specifically includes cDNAs. The term includes DNAs (including cDNAs) and RNAs that contain one or more modified bases. Thus, DNAs or RNAs with backbones modified for stability or for other reasons are “polynucleotides” as that term is intended herein. Moreover, DNAs or RNAs comprising unusual bases, such as inosine, or modified bases, such as tritiated bases, are included within the term “polynucleotides” as defined herein. In general, the term “polynucleotide” embraces all chemically, enzymatically and/or metabolically modified forms of unmodified polynucleotides, as well as the chemical forms of DNA and RNA characteristic of viruses and cells, including simple and complex cells.

The term “oligonucleotide” refers to a relatively short polynucleotide, including, without limitation, single-stranded deoxyribonucleotides, single- or double-stranded ribonucleotides, RNA:DNA hybrids and double-stranded DNAs. Oligonucleotides, such as single-stranded DNA probe oligonucleotides, are often synthesized by chemical methods, for example using automated oligonucleotide synthesizers that are commercially available. However, oligonucleotides can be made by a variety of other methods, including in vitro recombinant DNA-mediated techniques and by expression of DNAs in cells and organisms.

The term “gene expression” describes the conversion of the DNA gene sequence information into transcribed RNA (the initial unspliced RNA transcript or the mature mRNA) or the encoded protein product. Gene expression can be monitored by measuring the levels of either the entire RNA or protein products of the gene or subsequences.

The phrase “gene amplification” refers to a process by which multiple copies of a gene or gene fragment are formed in a particular cell or cell line. The duplicated region (a stretch of amplified DNA) is often referred to as “amplicon.” Often, the amount of the messenger RNA (mRNA) produced, i.e., the level of gene expression, also increases in the proportion of the number of copies made of the particular gene expressed.

Prognostic factors are those variables related to the natural history of breast cancer, which influence the recurrence rates and outcome of patients once they have developed breast cancer. Clinical parameters that have been associated with a worse prognosis include, for example, lymph node involvement, increasing tumor size, and high grade tumors. Prognostic factors are frequently used to categorize patients into subgroups with different baseline relapse risks. In contrast, treatment predictive factors are variables related to the likelihood of an individual patient's beneficial response to a treatment, such as anti-estrogen or chemotherapy, independent of prognosis.

The term “prognosis” is used herein to refer to the likelihood of cancer-attributable death or cancer progression, including recurrence and metastatic spread of a neoplastic disease, such as breast cancer, during the natural history of the disease. Prognostic factors are those variables related to the natural history of a neoplastic diseases, such as breast cancer, which influence the recurrence rates and disease outcome once the patient developed the neoplastic disease, such as breast cancer. In this context, “natural outcome” means outcome in the absence of further treatment. For example, in the case of breast cancer, “natural outcome” means outcome following surgical resection of the tumor, in the absence of further treatment (such as, chemotherapy or radiation treatment). Prognostic factors are frequently used to categorize patients into subgroups with different baseline risks, such as baseline relapse risks.

The term “prediction” is used herein to refer to the likelihood that a patient will respond either favorably or unfavorably to a drug or set of drugs, and also the extent of those responses. Thus, treatment predictive factors are those variables related to the response of an individual patient to a specific treatment, independent of prognosis. The predictive methods of the present invention can be used clinically to make treatment decisions by choosing the most appropriate treatment modalities for any particular patient. The predictive methods of the present invention are valuable tools in predicting if a patient is likely to respond favorably to a treatment regimen, such as anti-estrogen therapy, such as TAM treatment alone or in combination with chemotherapy and/or radiation therapy.

The term “long-term” survival is used herein to refer to survival for at least 3 years, more preferably for at least 8 years, most preferably for at least 10 years following surgery or other treatment.

The term “tumor,” as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.

The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Examples of cancer include, but are not limited to, breast cancer, ovarian cancer, colon cancer, lung cancer, prostate cancer, hepatocellular cancer, gastric cancer, pancreatic cancer, cervical cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, and brain cancer.

The “pathology” of cancer includes all phenomena that compromise the well-being of the patient. This includes, without limitation, abnormal or uncontrollable cell growth, metastasis, interference with the normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression or aggravation of inflammatory or immunological response, neoplasia, premalignancy, malignancy, invasion of surrounding or distant tissues or organs, such as lymph nodes, etc.

In the context of the present invention, reference to “at least one,” “at least two,” “at least three,” “at least four,” “at least five,” etc. of the genes listed in any particular gene set means any one or any and all combinations of the genes listed.

The term “node negative” cancer, such as “node negative” breast cancer, is used herein to refer to cancer that has not spread to the lymph nodes.

The terms “splicing” and “RNA splicing” are used interchangeably and refer to RNA processing that removes introns and joins exons to produce mature mRNA with continuous coding sequence that moves into the cytoplasm of an eukaryotic cell.

In theory, the term “exon” refers to any segment of an interrupted gene that is represented in the mature RNA product (B. Lewin. Genes IV Cell Press, Cambridge Mass. 1990). In theory the term “intron” refers to any segment of DNA that is transcribed but removed from within the transcript by splicing together the exons on either side of it. Operationally, exon sequences occur in the mRNA sequence of a gene as defined by Ref. SEQ ID numbers. Operationally, intron sequences are the intervening sequences within the genomic DNA of a gene, bracketed by exon sequences and having GT and AG splice consensus sequences at their 5′ and 3′ boundaries.

B. Detailed Description

The practice of the present invention will employ, unless otherwise indicated, conventional techniques of molecular biology (including recombinant techniques), microbiology, cell biology, and biochemistry, which are within the skill of the art. Such techniques are explained fully in the literature, such as, “Molecular Cloning: A Laboratory Manual”, 2^(nd) edition (Sambrook et al., 1989); “Oligonucleotide Synthesis” (M. J. Gait, ed., 1984); “Animal Cell Culture” (R. I. Freshney, ed., 1987); “Methods in Enzymology” (Academic Press, Inc.); “Handbook of Experimental Immunology”, 4^(th) edition (D. M. Weir & C. C. Blackwell, eds., Blackwell Science Inc., 1987); “Gene Transfer Vectors for Mammalian Cells” (J. M. Miller & M. P. Calos, eds., 1987); “Current Protocols in Molecular Biology” (F. M. Ausubel et al., eds., 1987); and “PCR: The Polymerase Chain Reaction”, (Mullis et al., eds., 1994). The practice of the present invention will also employ, unless otherwise indicated, conventional techniques of statistical analyis such as the Cox Proportional Hazards model (see, e.g. Cox, D. R., and Oakes, D. (1984), Analysis of Survival Data, Chapman and Hall, London, N.Y.). Such techniques are explained fully in the literature.

B.1. General Description of the Invention

As discussed before, the present invention is based, at least in part, on the recognition that since estrogens may contribute to tumorigenesis and tumor progression via pathways that are ESR1 independent, treatment decisions based primarily or solely on the ESR1 status of a patient are unsatisfactory.

Estrogen Metabolism

It is known that certain pathways of estrogen degradation involve the production of electrophilic estrogen metabolites as well as reactive oxygen species (ROS), both of which have the potential to damage cellular DNA and thus contribute to carcinogenesis (Cavalieri et al., Cell. Mol. Life Sci. 59: 665-81 (2002); Thompson and Ambrosone, J. Natl. Cancer Inst. 27: 125-34 (2000)).

The present invention is based on the identification of genes that are believed to be involved in the metabolism and/or clearance of estrogen, and thus in the control of intracellular concentration of electrophilic estrogen metabolites. In a specific embodiment, gene specific probe primer sets were designed based on the exon and introns sequences of the genes identified. These probe primer sets may be used in conjuction with a variety of clinical samples to identify particular genes within the estrogen metabolism group which are prognostic of outcome in a particular type of cancer and/or have predictive value in determining patient response to a particular treatment modality.

Estrogens, including the principle active hormones, estrone and estradiol, can be converted to catechol estrogens (CE) via either 2-hydroxylation by cytocchrome P4501A1 (CYP1A1) or via 4-hydroxylation by cytochrome P4501B1 (CYP1B1). These catechol estrogens (CE) can be further metabolized to CE semiquinones and then to CE quinones, which compounds are electrophiles and are proven or potential mutagens. (Mitrunen and Hirvonen, Mutation Research, 544: 9-41 (2003); Lieher, Endocrine Reviews, 21:40-54 (2000)). Furthermore, concomitant with the conversion of estrogen semiquinones to estrogen quinones, molecular oxygen is converted to highly reactive superoxide anion, which also can damage DNA.

The presence of electrophilic estrogen metabolites and reactive oxygen species could cause mutations in normal cells over time, resulting in tumorigenesis and could further cause new mutations in existing tumor cells that may be already compromised in their ability to repair damage to their DNA. The resulting increased burden of mutations could result in emergence of more aggressive clones in the tumor, more tumor aneuploidy and heterogeneity, with negative consequences for the health of the patient. Cellular metabolic strategies that would minimize the formation of mutagenic estrogen metabolites or increase the efficiency of their removal via conversion or clearance would then minimize mutagenic effects and result in more favorable prognosis.

Although a number of studies have been carried out to determine the effect on breast cancer predisposition risk of allelic variation in estrogen metabolizing genes, little has been done regarding the potential effect on cancer predisposition or prognosis, of expression levels of the various genes that affect cellular levels of mutatgenic estrogen metabolites.

One alternative to the cateechol/quinone pathway discussed above is the conversion, by the enzyme cathecol-O-methyl transferase (COMT), of estrogen catechols to 2-methoxy and 4-methoxy estrogens, compounds that are much less reactive than the quinones and more readily cleared from the cell.

Mutagenic catechol estrogen quinones can be converted back to catechol estrogens through the action of a NADPH-dependent quinone reductase (CRYZ), making them re-available for metabolism via COMT.

Direct clearance of both CE semiquinones and CE quinones can be initiated by conjugation of the metabolites with glutathione catalyzed by glutathione-S-transferase (GST) enzymes. The GST protein family includes GST mu enzymes (GSTM1, GSTM2, GSTM3, GSTM4 and GSTM5), GST pi enzyme GSTP1 and GST theta enzyme GSTT1. In addition to the above enzymes, membrane-associated glutathione-S-transferase enzymes that catalyze the conjugation of glutathione to electrophiles, including MGST1 and MGST3, have been identified. Membrane-associated glutathione-S-transferase may also catalyze the reduction of lipid hydroperoxides (see below).

Glutathione, required by GST enzymes, is a tripeptide synthesized from amino acids in a process the rate-limiting step of which is catalyzed by gamma-glutamylcysteine synthetase, an enzyme composed of a catalytic subunit (GCLC) and a regulatory subunit (GCLM) that are endoded by separate genes.

Various other metabolites arising from the synthesis and degradation of estrogens are further modified by enzymatic sulfation or glucuronidation as a prerequisite for their clearance from the cell. Variation in the levels of the enzymes that carry out these modifications may shift the intracellular concentrations of estrogen and its electrophilic metabolites. For example, SULT1E1 is a member of the sulfotransferase family that preferentially sulfates estrone at the 3 position in a detoxification and clearance step. Another family of proteins, the UDP-glucuronosyltransferases (UGTs), participates in the clearance of a wide variety of compounds, and includes UGT1A3 and UGT2b7, the substrates of which include estrone and 2-hydroxyestrone.

The forward and reverse conversion between catechol estrogens and catechol estrogen quinones establishes the possibility of redox cycling, which results in continuous generation of superoxide anion (O₂ ⁻) Cells have established strategies for detoxification of O₂ ⁻ produced by estrogen metabolism and other cellular processes. O₂ ⁻ is initially converted to molecular oxygen (O₂)+hydrogen peroxide (H₂O₂), another ROS, by a superoxide dismutase (SOD), which occur in cytoplamic (SOD1), mitochondrial (SOD2), and extracellular (SOD3) forms. The H₂O₂ produced by superoxide dismutase is further metabolized to H₂O₂ and molecular oxygen by catalase (CAT). The various enzymes of the peroxiredoxin family, including peroxiredoxins 2,3,4 and 6 (PRDX2, PRDX3, PRDX4 and PRDX6) also catalyze the inactivation of H₂O₂ as well as the reduction of organic hydroperoxides that may have been generated in the presence of ROS. Glutathione peroxidases (GPX1 and GPX2) are also involved in the detoxification of H₂O₂. Allelic variants of GPX1 have been associated with breast cancer risk (Knight et al., Cancer Epidemiol. Biomarkers Prev. 13: 146-9 (2004).

Hydrogen peroxide, in the presence of certain transition metal ions, gives rise to hydroxide ions, which not only can damage DNA directly but can also initiate lipid peroxidation, giving rise to lipid hydroperoxides. These lipid hydroperoxides are believed to accelerate the conversion of catechol estrogen to semiquinones and quinones by cytochrome P450 (Cavalieri CMLS), thus amplifying the production of electrophilic estrogen metabolites. Both peroxiredoxins (in addition to inactivating H202) and membrane-associated glutathione-S-transferases (in addition to conjugating glutathione to electrophilic estrogen metabolites) can catalyze the reduction of organic hydroperoxides by the action of ROS and therefore slow the procution of CE semiquinones and CE quinines.

The concentration of estrogen metabolites is affected by the rate estrogen synthesis as well as the routes and rates of degradation and clearance. Estrogen is synthesized from cholesterol via a complex series of reactions. Cholesterol is first metabolized in C21 steroid metabolism pathways to pregnenolone. As shown in FIG. 7, pregnenolone is then converted to androst-4-ene-3,17-dione by the action of a 3β-hydroxysteroid dehydrogenase and a cytochrome P450 (CYP17A1) in either order. Androst-4-ene-3,17-dione then gives rise to the key estrogens, estrone and estradiol through the sequential actions of a 17β-hydroxysteroid dehydrogenase (HSD17B1, HSD17B2, and HSD17B4) and the cytochrome P450 enzyme, aromatase (CYP19A1) in either order. Both estrone and estradiol are subject to the degradation processes discussed above.

Entry of estrogen precursors into the estrogen synthesis pathway can be limited by the alternate conversion of pregnenolone to progesterone and then to 20α-hydroxyprogesterone by 20α-hydroxysteroid dehydrogenase (AKR1C3), reducing the amount of androst-4-ene-3,17-dione available for conversion to estrogens.

The Invention

The present invention takes the novel approach of measuring the mRNA expression level of numerous genes that can affect the cellular concentration of mutagenic estrogen metabolites at equilibrium, and identifying markers of predisposition and prognosis in cancer the pathogenesis of which involves estrogen metabolism, such as breast cancer.

In particular, quantitative gene expression analysis performed in accordance with the present invention resulted in the identification of molecular indicators of prognosis in cancer. Based on analysis of the relationship between gene expression in the sample set and DRFS, a set of genes has been identified, the expression levels of which are indicative of outcome after tumor resection and any accompanying therapy with tamoxifen and/or adjuvant chemotherapy. Outcome may be manifest in various measurements including survival, recurrence-free survival and distant recurrence-free survival (DRFS), all of which are within the scope of the invention.

The genes identified in accordance with the present invention, or any gene group formed by particular combination of such genes can be used alone, or can be used together with one or more further diagnostic, prognostic and/or predictive indicators. Other diagnostic, prognostic and predictive indicators may include the expression of other genes or gene groups and may also include clinical variables including tumor size, stage and grade. Other diagnostic, prognostic or predictive indicators specifically include, individually or in any combination, the genes and genes sets disclosed in any of the following PCT Publications: WO 2003/078,662; WO 2004/071,572; WO 2004/074,518; WO 2004/065,583; WO 2004/111,273; WO 2004/111,603; WO 2005/008,213; WO 2005/040,396; WO 2005/039,382; WO 2005/064,019.

Alone or in combination with other cancer markers, such as diagnostic, prognostic and/or predictive indicators, the genes and gene groups of the present invention can be used to calculate Recurrence Score, an aggregate indication, based on multiple prognostic indicators, of the likelihood of a particular clinical outcome and/or drug responsiveness. Thus, for example, for an individual patient it is possible to provide a quantitative estimate of likelihood of outcome. This information can be utilized by the patient and treating physicians to make treatment decisions, in particular decisions regarding whether or not to treat the patient with drugs that lead to appreciable adverse events.

In various embodiments of the inventions, various technological approaches are available for determination of expression levels of the disclosed genes, including, without limitation, RT-PCR, microarrays, serial analysis of gene expression (SAGE) and Gene Expression Analysis by Massively Parallel Signature Sequencing (MPSS), which will be discussed in detail below. In particular embodiments, the expression level of each gene may be determined in relation to various features of the expression products of the gene including exons, introns, protein epitopes and protein activity. In other embodiments, the expression level of a gene may be inferred from analysis of the structure of the gene, for example from the analysis of the methylation pattern of gene's promoter(s).

B.2 Gene Expression Profiling

In general, methods of gene expression profiling can be divided into two large groups: methods based on hybridization analysis of polynucleotides, and methods based on sequencing of polynucleotides. The most commonly used methods known in the art for the quantification of mRNA expression in a sample include northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106:247-283 (1999)); RNAse protection assays (Hod, Biotechniques 13:852-854 (1992)); and reverse transcription polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8:263-264 (1992)). Alternatively, antibodies may be employed that can recognize specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA-protein duplexes. Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression (SAGE), and gene expression analysis by massively parallel signature sequencing (MPSS).

a. Reverse Transcriptase PCR (RT-PCR)

Of the techniques listed above, the most sensitive and, most flexible quantitative method is RT-PCR, which can be used to compare mRNA levels in different sample populations, in normal and tumor tissues, with or without drug treatment, to characterize patterns of gene expression, to discriminate between closely related mRNAs, and to analyze RNA structure.

The first step is the isolation of mRNA from a target sample. The starting material is typically total RNA isolated from human tumors or tumor cell lines, and corresponding normal tissues or cell lines, respectively. Thus RNA can be isolated from a variety of primary tumors, including breast, lung, colon, prostate, brain, liver, kidney, pancreas, spleen, thymus, testis, ovary, uterus, etc., tumor, or tumor cell lines, with pooled DNA from healthy donors. If the source of mRNA is a primary tumor, mRNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples.

General methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A (1987), and De Andrés et al., BioTechniques 18:42044 (1995). In particular, RNA isolation can be performed using purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Other commercially available RNA isolation kits include MasterPure™ Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.), and Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from tumor can be isolated, for example, by cesium chloride density gradient centrifugation.

As RNA cannot serve as a template for PCR, the first step in gene expression profiling by RT-PCR is the reverse transcription of the RNA template into cDNA, followed by its exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avilo myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MMLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the circumstances and the goal of expression profiling. For example, extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, Calif., USA), following the manufacturer's instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.

Although the PCR step can use a variety of thermostable DNA-dependent DNA polymerases, it typically employs the Taq DNA polymerase, which has a 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonuclease activity. Thus, TaqMan® PCR typically utilizes the 5′-nuclease activity of Taq or Tth polymerase to hydrolyze a hybridization probe bound to its target amplicon, but any enzyme with equivalent 5′ nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon typical of a PCR reaction. A third oligonucleotide, or probe, is designed to detect nucleotide sequence located between the two PCR primers. The probe is non-extendible by Taq DNA polymerase enzyme, and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Any laser-induced emission from the reporter dye is quenched by the quenching dye when the two dyes are located close together as they are on the probe. During the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resultant probe fragments disassociate in solution, and signal from the released reporter dye is free from the quenching effect of the second fluorophore. One molecule of reporter dye is liberated for each new molecule synthesized, and detection of the unquenched reporter dye provides the basis for quantitative interpretation of the data.

TaqMan® RT-PCR can be performed using commercially available equipment, such as, for example, ABI PRISM 7700™ Sequence Detection System™ (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In a preferred embodiment, the 5′ nuclease procedure is run on a real-time quantitative PCR device such as the ABI PRISM 7700™ Sequence Detection System™. The system consists of a thermocycler, laser, charge-coupled device (CCD), camera and computer. The system amplifies samples in a 96-well format on a thermocycler. During amplification, laser-induced fluorescent signal is detected at the CCD. The system includes software for running the instrument and for analyzing the data.

5′-Nuclease assay data are initially expressed as C_(T), or the threshold cycle. As discussed above, fluorescence values are recorded during every cycle and represent the amount of product amplified to that point in the amplification reaction. The point when the fluorescent signal is first recorded as statistically significant is the threshold cycle (C_(T)).

To minimize errors and the effect of sample-to-sample variation, RT-PCR is usually performed using one or more reference genes as internal standards. The ideal internal standard is expressed at a constant level among different tissues, and is unaffected by the experimental treatment. RNAs most frequently used to normalize patterns of gene expression are mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPD) and β-actin (ACTB).

A more recent variation of the RT-PCR technique is real time quantitative RT-PCR (q RT-PCR), which measures PCR product accumulation through a dual-labeled fluorigenic probe (i.e., TaqMan® probe). Real time PCR is compatible both with quantitative competitive PCR, where internal competitor for each target sequence is used for normalization, and with quantitative comparative PCR using a normalization gene contained within the sample, or a housekeeping gene for RT-PCR. For further details see, e.g. Held et al., Genome Research 6:986-994 (1996).

The steps of a representative protocol for profiling gene expression using fixed, paraffin-embedded tissues as the RNA source, including mRNA isolation, purification, primer extension and amplification are given in various published journal articles {for example: T. E. Godfrey et al. J. Molec. Diagnostics 2: 84-91 (2000); K. Specht et al., Am. J. Pathol. 158: 419-29 (2001); Cronin et al., Am J Pathol 164:35-42 (2004)}. Briefly, a representative process starts with cutting about 10 μm thick sections of paraffin-embedded tumor tissue samples. The RNA is then extracted, and protein and DNA are removed. After analysis of the RNA concentration, RNA repair and/or amplification steps may be included, if necessary, and RNA is reverse transcribed using gene specific promoters followed by RT-PCR.

b. Microarrays

Differential gene expression can also be identified, or confirmed using the microarray technique. Thus, the expression profile of breast cancer-associated genes can be measured in either fresh or paraffin-embedded tumor tissue, using microarray technology. In this method, polynucleotide sequences of interest (including cDNAs and oligonucleotides) are plated, or arrayed, on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. Just as in the RT-PCR method, the source of mRNA typically is total RNA isolated from human tumors or tumor cell lines, and corresponding normal tissues or cell lines. Thus RNA can be isolated from a variety of primary tumors or tumor cell lines. If the source of mRNA is a primary tumor, mRNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples, which are routinely prepared and preserved in everyday clinical practice.

In a specific embodiment of the microarray technique, PCR amplified inserts of cDNA clones are applied to a substrate in a dense array. Preferably at least 10,000 nucleotide sequences are applied to the substrate. The microarrayed genes, immobilized on the microchip at 10,000 elements each, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pairwise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. The miniaturized scale of the hybridization affords a convenient and rapid evaluation of the expression pattern for large numbers of genes. Such methods have been shown to have the sensitivity required to detect rare transcripts, which are expressed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels (Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106-149 (1996)). Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GenChip technology, or Incyte's microarray technology.

The development of microarray methods for large-scale analysis of gene expression makes it possible to search systematically for molecular markers of cancer classification and outcome prediction in a variety of tumor types.

c. Serial Analysis of Gene Expression (SAGE)

Serial analysis of gene expression (SAGE) is a method that allows the simultaneous and quantitative analysis of a large number of gene transcripts, without the need of providing an individual hybridization probe for each transcript. First, a short sequence tag (about 10-14 bp) is generated that contains sufficient information to uniquely identify a transcript, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form long serial molecules, that can be sequenced, revealing the identity of the multiple tags simultaneously. The expression pattern of any population of transcripts can be quantitatively evaluated by determining the abundance of individual tags, and identifying the gene corresponding to each tag. For more details see, e.g. Velculescu et al., Science 270:484-487 (1995); and Velculescu et al., Cell 88:243-51 (1997).

d. Gene Expression Analysis by Massively Parallel Signature Sequencing (MPSS)

This method, described by Brenner et al., Nature Biotechnology 18:630-634 (2000), is a sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 μm diameter microbeads. First, a microbead library of DNA templates is constructed by in vitro cloning. This is followed by the assembly of a planar array of the template-containing microbeads in a flow cell at a high density (typically greater than 3×10⁶ microbeads/cm²). The free ends of the cloned templates on each microbead are analyzed simultaneously, using a fluorescence-based signature sequencing method that does not require DNA fragment separation. This method has been shown to simultaneously and accurately provide, in a single operation, hundreds of thousands of gene signature sequences from a yeast cDNA library.

e. General Description of the mRNA Isolation, Purification and Amplification

The steps of a representative protocol for profiling gene expression using fixed, paraffin-embedded tissues as the RNA source, including mRNA isolation, purification, primer extension and amplification are provided in various published journal articles (for example: T. E. Godfrey et al,. J. Molec. Diagnostics 2: 84-91 [2000]; K. Specht et al., Am. J. Pathol. 158: 419-29 [2001]). Briefly, a representative process starts with cutting about 10 μm thick sections of paraffin-embedded tumor tissue samples. The RNA is then extracted, and protein and DNA are removed. After analysis of the RNA concentration, RNA repair and/or amplification steps may be included, if necessary, and RNA is reverse transcribed using gene specific-promoters followed by RT-PCR. Finally, the data are analyzed to identify the best treatment option(s) available to the patient on the basis of the characteristic gene expression pattern identified in the tumor sample examined, dependent on the predicted likelihood of cancer recurrence.

f. Reference Gene Set

An important aspect of the present invention is to use the measured expression of certain genes by breast cancer tissue to provide prognostic or predictive information. For this purpose it is necessary to correct for (normalize away) both differences in the amount of RNA assayed and variability in the quality of the RNA used. Well known housekeeping genes such as β-actin, GAPD, GUS, RPLO, and TFRC can be used as reference genes for normalization. Reference genes can also be chosen based on the relative invariability of their expression in the study samples and their lack of correlation with clinical outcome. Alternatively, normalization can be based on the mean or median signal (C_(T)) of all of the assayed genes or a large subset thereof (global normalization approach). Below, unless noted otherwise, gene expression means normalized expression.

g. Primer and Probe Design

According to one aspect of the present invention, PCR primers and probes are designed based upon intron sequences present in the gene to be amplified. Accordingly, the first step in the primer/probe design is the delineation of intron sequences within the genes. This can be done by publicly available software, such as the DNA BLAT software developed by Kent, W. J., Genome Res. 12(4):656-64 (2002), or by the BLAST software including its variations. Subsequent steps follow well established methods of PCR primer and probe design.

In order to avoid non-specific signals, it is important to mask repetitive sequences within the introns when designing the primers and probes. This can be easily accomplished by using the Repeat Masker program available on-line through the Baylor College of Medicine, which screens DNA sequences against a library of repetitive elements and returns a query sequence in which the repetitive elements are masked. The masked intron sequences can then be used to design primer and probe sequences using any commercially or otherwise publicly available primer/probe design packages, such as Primer Express (Applied Biosystems); MGB assay-by-design (Applied Biosystems); Primer3 (Steve Rozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general users and for biologist programmers. In: Krawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, N.J., pp 365-386).

The most important factors considered in PCR primer design include primer length, melting temperature (Tm), and G/C content, specificity, complementary primer sequences, and 3′-end sequence. In general, optimal PCR primers are generally 17-30 bases in length, and contain about 20-80%, such as, for example, about 50-60% G+C bases. Tm's between 50 and 80° C., e.g. about 50 to 70° C. are typically preferred.

For further guidelines for PCR primer and probe design see, e.g. Dieffenbach, C. W. et al., “General Concepts for PCR Primer Design” in: PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press, New York, 1995, pp. 133-155; Innis and Gelfand, “Optimization of PCRs” in: PCR Protocols, A Guide to Methods and Applications, CRC Press, London, 1994, pp. 5-11; and Plasterer, T. N. Primerselect: Primer and probe design. Methods Mol. Biol. 70:520-527 (1997), the entire disclosures of which are hereby expressly incorporated by reference.

B.3 Sources of Biological Material

Treatment of cancer often involves resection of the tumor to the extent possible without severely compromising the biological function of the patient. As a result, tumor tissue is typically available for analysis following initial treatment of the tumor, and this resected tumor has most often been the sample used in expression analysis studies.

Expression analysis can also be carried out on tumor tissue obtained through other means such as core, fine needle, or other types of biopsy.

For particular tumor types, tumor tissue is appropriately obtained from biological fluids using methods such as fine needle aspiration, bronchial lavage, or transbronchial biopsy.

Particularly in relatively advanced tumors, circulating tumor cells (CTC) are sometimes found in the blood of cancer patients. CTC recovered from blood can also be used as a source of material for expression analysis.

Cellular constituents, including RNA and protein, derived from tumor cells have been found in biological fluids of cancer patients, including blood and urine. Circulating nucleic acids and proteins may result from tumor cell lysis and may be subjected to expression analysis.

B.3 Algorithms and Statistical Methods

When quantitative RT-PCR (qRT-PCR) is used to measure mRNA levels, mRNA amounts are expressed in C_(T) (threshold cycle) units (Held et al., Genome Research 6:986-994 (1996)). The averaged sum of CTS for the reference mRNAs is arbitrarily set (e.g. to zero), and each measured test mRNA C_(T) is given relative to this fixed reference. For example, if, for a particular patient tumor specimen the average of C_(T)s of the reference genes found to be 31 and C_(T) of test gene X is found to be 35, the reported value for gene X is −4 (i.e. 31−35).

The normalized data can be used to analyze correlation between the expression level of particular mRNAs and clinical outcome. Standard statistical methods can be applied to identify those genes, for which the correlation between expression and outcome, in a univariate analysis, is statistically significant. These genes are markers of outcome, given the existing clinical status. Multivariate analysis can be applied to identify sets of genes, the expression levels of which, when used in combination, are better markers of outcome than the individual genes that constitute the sets.

Further, it is possible to define groups of genes known or suspected to be associated with particular aspects of the molecular pathology of cancer. A gene can be assigned to a particular group based either on its known or suspected role in a particular aspect of the molecular biology of cancer or based on its co-expression with another gene already assigned to a particular group. Co-pending U.S. Patent Application 60/561,035 defines several such groups and further shows that the definition of such groups (also termed axis or subset) is useful in that it supports particular methods of data analysis and the elaboration of mathematical algorithms, which in turn yields a more powerful predictors of outcome than can be formulated if these groups are not defined.

In breast cancer, steroid metabolism, including synthesis and degradation of steroids and clearance of intermediates is an aspect of the molecular pathology of cancer the importance of which has not been adequately appreciated. Genes involved in steroid metabolism form a “Steroid Metabolism Group” the definition of which supports particular methods of data analysis and will support the elaboration of mathematical algorithms useful in the prediction of outcome in various forms of cancer. The precise definition of the genes in the “Steroid Metabolism Group may vary depending on the identity of the steroid relevant in a particular cancer but will be defined to include a) genes, the expression products of which are known or suspected to be involved in synthesis and degradation of the particular steroid and clearance of intermediates, and b) genes that are co-expressed with such genes.

B.5 Clinical Application of Data

The methods of this invention could be performed as a self-contained test for cancer. Individual markers of the invention identified by univariate analysis or sets of markers of the inventions (e.g. identified by multivariate analysis) are useful predictors of clinical outcome. Alternatively the markers can be applied as predictive elements of a test that could include other predictive indicators including a) other genes and/or gene groups, or b) other clinical indicators such as tumor stage and grade).

B.6 Kits of the Invention

The methods of this invention, when practiced for commercial diagnostic purposes would typically be performed in a CLIA-approved clinical diagnostic laboratory. The materials for use in the methods of the present invention are suited for preparation of kits produced in accordance with well known procedures. The invention thus provides kits or components thereof, such kits comprising agents, which may include gene-specific or gene-selective probes and/or primers, for quantitating the expression of the disclosed genes for predicting prognostic outcome or response to treatment. Such kits may optionally contain reagents for the extraction of RNA from tumor samples, in particular fixed paraffin-embedded tissue samples and/or reagents for RNA amplification. In addition, the kits may optionally comprise the reagent(s) with an identifying description or label or instructions relating to their use in the methods of the present invention. The kits may comprise containers (including microtiter plates suitable for use in an automated implementation of the method), each with one or more of the various reagents (typically in concentrated form) utilized in the methods, including, for example, pre-fabricated microarrays, buffers, the appropriate nucleotide triphosphates (e.g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP), reverse transcriptase, DNA polymerase, RNA polymerase, and one or more probes and primers of the present invention (e.g., appropriate length poly(T) or random primers linked to a promoter reactive with the RNA polymerase). Mathematical algorithms used to estimate or quantify prognostic or predictive information are also properly potential components of kits.

The methods provided by the present invention may also be automated in whole or in part.

All aspects of the present invention may also be practiced such that a limited number of additional genes that are co-expressed with the disclosed genes, for example as evidenced by high Pearson correlation coefficients, are included in a prognostic or predictive test in addition to and/or in place of disclosed genes.

Having described the invention, the same will be more readily understood through reference to the following Example, which is provided by way of illustration, and is not intended to limit the invention in any way.

EXAMPLES Example 1 Multiple GSTM1 Gene Family Members as Recurrance Risk Markers

Breast Tumor FPE Specimens. Archival breast tumor FPE blocks, from patients diagnosed between 1990 and 1997, were provided by Providence St. Joseph Medical Center, Burbank Calif. and were a subset of specimens examined in a previously reported observational study [Esteban, J. et al. Prog. Proc Am Soc. Clin. Oncol. 22, 850 abstract (2003)]. The tumor tissue specimens all came from female breast cancer patients with primary disease (90% stage I or II) and relatively little nodal involvement (80% node negative). The protocol for use of these specimens was approved by the IRB of that medical center.

Human genomic DNA samples. Genomic DNA was supplied by Dr. Maureen Cronin. The samples were collected with informed consent for genotyping under an IRB approved protocol.

RNA extraction and preparation. RNA was extracted from three 10 μm FPE sections per patient specimen according to Cronin et al. [Am. J. Pathol. 164, 35-42 (2004)].

RNA amplification. The FPE RNA used in this study was amplified prior to RT-PCR assay in order to preserve the RNA for later studies. Fifty ng of each FPE RNA sample was amplified using the SenseAmp kit from Genisphere (Hatfield, Pa.). The amplified RNA products were purified using, the mirVana miRNA isolation kit from Ambion.

TaqMan primer/probe design. Exon-based assays: mRNA reference sequence accession numbers for genes of interest were identified and used to access the sequences through the NCBI Entrez Nucleotide database. Intron-based assays. Intron sequences were delineated by aligning appropriate mRNA reference sequences with their corresponding genes by using the DNA BLAT software [Kent, W. J., Genome Res. 12, 656-664(2002)]. Repetitive sequences within the introns were identified and masked using the Repeat Masker program (Institute for Systems Biology). Primers and probes were designed using Primer Express 2.0 (Applied Biosystems, Foster City, Calif.), or Primer 3 [Rozen, R. & Skaletsky, H. J. In Krawetz, S, Misener, S (eds) Bioinformatics Methods and Protocols:Methods in Molecular Biology. Humana Press, Totowa, N.J., 365-386(2000)]. Standard chemistry oligonucleotides were supplied by Biosearch Technologies Inc. (Novato, Calif.), Integrated DNA Technologies (Coralville, Iowa), and Eurogentech (San Diego, Calif.); MGB probes were supplied by Applied Biosystems. Amplicon sizes were typically 60-85 bases in length. Fluorogenic probes were dual-labeled with 5′-FAM and 3′-BHQ-2.

Reverse Transcription and TaqMan gene expression profiling RT-PCR was carried out as previously described [Cronin et al. Am.J.Pathol. 164, 35-42 (2004)].

Normalization and data analysis. Reference gene-based normalization was used to correct for differences in RNA quality and total quantity of RNA assayed. A set of five reference genes were selected from a series of candidates based on their low variance in expression across all the FPE breast cancer tissues and absence of a relationship (p>0.25) with disease free survival. A reference CT for each tested tissue was defined as the average measured CT of the five reference genes. The normalized mRNA level of a test gene within a tissue specimen was defined by the difference between the average CT of the test gene (from triplicate measurements) minus the reference CT.

Statistical analysis. Least squares linear regression was used to model the relationship between the levels of pairs of assays. Pearson's correlation coefficient was used to summarize the strength of the linear relationship. Cox Proportional Hazards regression was used to model the relationship between gene expression levels and disease-free survival, which was defined as the time from surgical removal of the breast tumor until the recurrence of breast cancer or death from breast cancer or an unknown cause.

The GSTM (GSTμ) gene family consists of five different closely related isotypes named GSTM1-GSTM5. We have reported four independent clinical studies in which GSTM gene expression strongly correlates with good outcome in primary breast cancer, based on measurements made using an RT-PCR probe-primer set (designated GSTM1.1) that was designed to recognize GSTM1 [8, Esteban, J. et al: Tumor gene expression and prognosis in breast cancer:multigene RT-PCR assay of paraffin-embedded tissue. Prog. Proc Am Soc. Clin. Oncol. 22, 850 abstract (2003), Cobleigh, M. A. et al. Tumor gene expression predicts distant disease-free survival (DDFS) in breast cancer patients with 10 or more positive nodes: high throughput RT-PCR assay of paraffin-embedded tumor tissues. Prog. Proc Am Soc. Clin. Oncol. 22, 850 abstract (2003), Paik, S. et al. Multi-gene RT-PCR assay for predicting recurrence in node negative breast cancer patients-NSABP studies B-20 and B-14. Breast Cancer Res. Treat. 82:A16 .abstract (2003)].

GSTM expression was examined by qRT-PCR in FPET primary breast cancer tissues. GSTM1 was detected with the GSTM1.1 assay, which recognizes several GSTM isotypes. The estimate of relative risk in studies 1-4 was based on the hazard ratio (HR) from analysis of the time to breast cancer recurrence using univariate Cox proportional hazards regression. The estimate of relative risk in study 5 was based on the odds ratio (OR) from analysis of breast cancer death in a matched case-control study using conditional logistic regression.

Study 1, Esteban et al., Prog. Proc Am Soc. Clin. Oncol. 22:850 abstract, 2003; Study 2, Cobleigh et al., Clin Cancer Res 11:8623-31,2005; Study 3, Paik et al., Breast Cancer Res. Treat. 82:A16 abstract, 2003; Study 4, Paik et al, N Engl J Med 351:2817-26, 2004; Study 5, Habel et. al, Breast-Cancer Res. Treat. 88:3019 abstract, 2004. The results are shown in Table 1.* Patients in studies 3-5 were tamoxifen treated, LN−,ER+. GSTM expression was a consistent predictor of favorable outcome in five independent breast cancer recurrence studies. TABLE 1 Rank Relative (among Total no. of Study Risk P-Value tested genes) genes tested 1 Providence 0.71 0.0014 6 192 2 Rush 0.80 0.0200 5 192 3 NSABP 20* 0.68 0.0005 7 192 4 NSABP 14* 0.73 <0.0001 5 21 (OncotypeDX) 5 Kaiser* 0.72 <0.0010 ≦6 21 (OncotypeDX)

Sequence alignments of GSTM1 and GSTM2 amplicons with corresponding regions of other GSTM family members (FIG. 1). Sequences were aligned by Clustal W (family member denoted in left column). Arrows mark forward (left) and reverse RT-PCR primer (right) regions. Sequences beneath horizontal line indicates probe region. Gray boxes highlight mismatches with primers/probes in the first column. The vertical line in GSTM1.1 indicates a spliced exon-exon junction. The vertical line in GSTM2int4.2 indicates an unspliced intron-exon junction. In fact, alignment of the targeted GSTM1 amplicon probe-primer set with homologous regions in GSTM2, GSTM4 and GSTM5 indicates only 1, 3 and 3 base mismatches, respectively, indicating that GSTM1.1 may also amplify those sequences (FIG. 1).

Consistent with the fact that 50% of the U.S. population is homozygous GSTM1-null, the GSTM1 intron-based assay displays a biphasic expression pattern within 125 breast cancer specimens. FIG. 2 shows the number of patients (Y-axis) and corresponding Ct values (x-axis) were plotted for GSTM1.1, GSTM1int5.2 and GSTM2int4.2 assays. Expression levels were determined by TaqMan RT-PCR. “int” indicates that the assay was derived from intron sequence.

It is noteworthy that a GSTM1.1 signal was detected in all specimens (C_(T)<40). This result is strong evidence that GSTM1.1 is not specific for GSTM1, because it is well-established that approximately 50% of the Caucasian and Asian populations are homozygous null for the GSTM1 gene. FIG. 2 shows that in the case of GSTM1int5.2, RT-PCR signals distribute in a bimodal pattern, with no signal detected in ˜50% of the specimens, consistent with specificity for GSTM1. GSTM1int3.1 showed a similar bimodal pattern as GSTM1int5. Furthermore, as shown in FIG. 3, genotyping of 22 independent human genomic DNA samples using GSTM1int5.2 identified ˜50% as GSTM1 null, (C_(T)=40). C_(T) values were ˜31-32 for the remaining samples. Again, GSTM1.1 failed to discriminate between the two GSTM1 genotypes, yielding C_(T)˜31-32 in all cases.

We also explored the expression of another GSTM isotype, GSTM2, using an intron-based design, designated GSTM2int4.2. This 73 base amplicon differs from the other GSTM isotypes by 14 or more bases within the corresponding primer/probe regions (FIG. 2). Expression of this sequence in the 125 patient specimens distributes across 6 C_(T) units, from 34-40 (FIG. 2). Genotyping with GSTM2int4.2 gave uniform positive signals for all 22 tested DNA specimens (FIG. 3) indicating that GSTM2 is not deleted.

Pearson (R) correlation between GSTM family members. Table 2 shows the Pearson (r) correlation for the various GStM gene family members as determined by various probe-primer sets. Bold font denotes R values between assay sets that we found to be specific for the designated genes. “int” indicates that the assay was derived from intron sequence. In general, the GSTM family members show positive correlations of expression. However, there is a wide range of correlations that vary not only between genes but also between probe-primer sets within the same gene. Among the probe-primer sets thought to be gene specific (bold font), correlations range from 0.15 to 0.91. GSTM1int3.1 and GSTM1int5.2 showed the highest degree of co-expression (R=0.91). Interestingly, GSTM3.5 and GSTM3.6 show a more modest correlation (R=0.68) suggesting perhaps that they monitor alternate GSTM3 transcripts that are differentially regulated. GSTM4.1 vs. GSTM5.2 and GSTM4.1 vs.GSTM1int5.2 show the lowest levels of coordinated expression (R=0.15-0.22) which was not unexpected since they are detecting transcripts from different genes. GSTM2int4.2 and GSTM3.6, the two genes that both contribute to positive prognosis in the multivariate analysis, show a modest positive correlation (0.42).

In summary, the positive effects of the GSTM family members are most likely due to a combination of protein function and co-expression. (Table 2). TABLE 2 GSTM1 GSTM1 Pearson (R) int int GSTM2 correlation 5.2 3.1 GSTM1.1 int 4.2 GSTM3.6 GSTM4.1 GSTM5.2 GSTM1 int 1.00 5.2 GSTM1 int 0.91 1.00 3.1 GSTM1.1 0.52 0.49 1.00 GSTM2 int 0.26 0.25 0.57 1.00 4.2 GSTM3.6 0.26 0.23 0.46 0.37 1.00 GSTM4.1 0.15 0.18 0.51 0.34 0.44 1.00 GSTM5.2 N/A N/A 0.19 0.23 0.23 0.15 1.00 GSTM5.1 0.29 0.28 0.40 0.28 0.27 0.22 N/A

GSTM1-5 expression predict favorable outcome in the 125 breast cancer specimen study. Multivariate analysis suggests that GSTM2 and GSTM3 carry independent biomarker information. Univariate and multivariate Cox PH regression analysis. Assays are ordered by p-value, with p-values <0.05 considered significant. Data in bold are assays that are specific. “int” indicates that the assay was derived from intron sequence. (Tables 3 and 4).

The tables indicate that all 5 GSTM genes are indicators of positive prognosis. The order of predictive strength from strongest to weakest is: GSTM3>GSTM2>GSTM4>GSTM5≧GSTM1. TABLE 3 Hazard HR HR Univariate Analysis Ratio 95% LCL 95% UCL P-Value GSTM3.6 0.57 0.42 0.78 0.0003 GSTM2 int 4.2 0.64 0.49 0.83 0.0003 GSTM1.1 0.71 0.58 0.86 0.0009 GSTM4.1 0.68 0.53 0.87 0.0044 GSTM1 int 5.2 0.79 0.64 0.96 0.0128 GSTM5.2 0.77 0.58 1.02 0.0493 GSTM1 int 3.1 0.84 0.70 1.02 0.0632

A multivariate stepwise Cox PH analysis indicated that GSTM3 and GSTM2 contributed independently to the positive prognosis (Table 4). Because there was an independent contribution to survival by both GSTM2 and GSTM3, it would suggest that each gene (product) has a biological effect. TABLE 4 Hazard HR HR Multivariate Analysis Ratio 95% LCL 95% UCL P-Value GSTM3.6 0.65 0.47 0.90 0.0105 GSTM2 int 4.2 0.74 0.58 0.95 0.0185

The results indicate that all five GSTM genes are correlated with the likelihood of breast cancer recurrence and suggest that certain GSTM family members contribute independent prognostic information.

Example 2

A Study of the Prognostic Value of GSTM Family Members and Estrogen Metabolizing Genes in Invasive Breast Cancer

The primary objective of this study was to determine the relationship between the expression of genes involved in estrogen metabolism (including members of the GST gene family) and clinical outcome, in particular distant recurrence-free survival (DRFS), in breast cancer carcinoma.

Study Design

Inclusion Criteria

Samples were initially obtained from patients meeting the following criteria.

Surgery performed with diagnosis of invasive ductal carcinoma of the breast, ductal carcinoma in situ (DCIS), lobular carcinoma of the breast, or lobular carcinoma in situ (LCIS).

Histopathologic assessment indicating adequate amounts of tumor tissue and homogeneous pathology for inclusion in this research study.

For each patient sample included in the study, the expression level of each of 82 amplicons (shown in Table 5) was quantitatively assessed using qRT-PCR and the correlation between gene expression and distant recurrence-free survival (DRFS) for each of the test genes was evaluated. Distant recurrence-free survival is the time from surgery until the first diagnosis of distant recurrence. Contralateral disease, other second primary cancers, and deaths prior to distant recurrence will be considered censoring events. For the primary analysis, ipsilateral breast recurrence, local chest wall recurrence and regional recurrence is ignored, i.e., not considered either as an event or a censoring event.

For this study, one hundred twenty five (125) tumor samples were chosen from the patients. All recurring patients were included in the study, as well as a randomly selected subset of patients who were censored (J. Esteban et al., ASCO Meeting Proceedings 22:850 (2003) (Abstract 3416)).

Gene Panel

A panel of genes potentially involved in metabolism or clearance of estrogen or in other aspects of cancer pathophysiology was compiled based on published literature. Analysis of 82 genes selected from this panel or potentially useful as reference genes and listed in Table 5 was carried out using quantitative RT-PCR. For certain of the genes, multiple probe primer sets targeted to distinct gene sequences were utilized. Gene names and primer and probe sequences used to quantify transcript expression are listed in Table 6. TABLE 5 Official NCBI Sequence Gene Official NCBI Sequence Symbol Sequence ID Version ID Symbol Sequence ID Version Gene ID AKR1C1 BC040210 BC040210.1 1645 IMMT NM_006839 NM_006839.1 10989 AKR1C2 NM_001354 NM_001354.4 1646 MKI67 NM_002417 NM_002417.2 4288 AKR1C3 NM_003739 NM_003739.4 8644 LIPA NM_000235 NM_000235.2 3988 ATP5A1 NM_004046 NM_004046.4 498 MDH2 NM_005918 NM_005918.2 4191 ACTB NM_001101 NM_001101.2 60 MGST1 NM_020300 NM_020300.3 4257 BCL2 NM_000633 NM_000633.1 596 MGST3 NM_004528 NM_004528.2 4259 CAT NM_001752 NM_001752.1 847 MPV17 NM_002437 NM_002437.3 4358 CD68 NM_001251 NM_001251.1 968 MVP NM_017458 NM_017458.2 9961 CDH1 NM_004360 NM_004360.2 999 NAT1 NM_000662 NM_000662.4 9 SCUBE2 NM_020974 NM_020974.1 57758 NAT2 NM_000015 NM_000015.1 10 COMT NM_000754 NM_000754.2 1312 NCOA2 NM_006540 NM_006540.1 10499 COX8A NM_004074 NM_004074.2 1351 NDUFA7 NM_005001 NM_005001.1 4701 CRYZ NM_001889 NM_001889.2 1429 NQO1 NM_000903 NM_000903.1 1728 CTSL2 NM_001333 NM_001333.2 1515 NQO2 NM_000904 NM_000904.1 4835 PPIH NM_006347 NM_006347.3 10465 TP53 NM_000546 NM_000546.2 7157 CYP17A1 NM_000102 NM_000102.2 1586 SERPINE1 NM_000602 NM_000602.1 5054 CYP19A1 NM_000103 NM_000103.2 1588 PGR NM_000926 NM_000926.2 5241 CYP1A1 NM_000499 NM_000499.2 1543 PRAME NM_006115 NM_006115.3 23532 CYP1B1 NM_000104 NM_000104.2 1545 PRDX2 NM_005809 NM_005809.4 7001 CYP4Z1 NM_178134 NM_178134.2 199974 PRDX3 NM_006793 NM_006793.2 10935 EPHX1 NM_000120 NM_000120.2 2052 PRDX4 NM_006406 NM_006406.1 10549 ESR1 NM_000125 NM_000125.1 2099 PRDX6 NM_004905 NM_004905.2 9588 FOXM1 NM_021953 NM_021953.2 2305 RPLP0 NM_001002 NM_001002.3 6175 GAPD NM_002046 NM_002046.2 2597 SC5DL NM_006918 NM_006918.2 6309 GCLC NM_001498 NM_001498.2 2729 SOD1 NM_000454 NM_000454.4 6647 GCLM NM_002061 NM_002061.2 2730 SOD2 NM_000636 NM_000636.1 6648 GPX1 NM_000581 NM_000581.2 2876 SOD3 NM_003102 NM_003102.1 6649 GPX2 NM_002083 NM_002083.2 2877 SRD5A2 NM_000348 NM_000348.2 6716 GSTM1 NM_000561 NM_000561.2 2944 STK6 NM_003600 NM_003600.2 6790 GSTM2 NM_000848 NM_000848.2 2946 SULT1E1 NM_005420 NM_005420.2 6783 GSTM3 NM_000849 NM_000849.3 2947 SULT4A1 NM_014351 NM_014351.2 25830 GSTM4 NM_000850 NM_000850.3 2948 BIRC5 NM_001168 NM_001168.2 332 GSTM5 NM_000851 NM_000851.2 2949 TBP NM_003194 NM_003194.2 6908 GSTP1 NM_000852 NM_000852.2 2950 TFRC NM_003234 NM_003234.1 7037 GSTT1 NM_000853 NM_000853.1 2952 TST NM_003312 NM_003312.4 7263 GUSB NM_000181 NM_000181.1 2990 UGT1A3 NM_019093 NM_019093.2 54659 HOXB13 NM_006361 NM_006361.2 10481 UGT2B7 NM_001074 NM_001074.1 7364 HSD17B1 NM_000413 NM_000413.1 3292 PLAU NM_002658 NM_002658.2 5328 HSD17B2 NM_002153 NM_002153.1 3294 VDAC1 NM_003374 NM_003374.1 7416 HSD17B4 NM_000414 NM_000414.1 3295 VDAC2 NM_003375 NM_003375.2 7417 IL17RB NM_018725 NM_018725.2 55540 XPC NM_004628 NM_004628.3 7508

TABLE 6 Accession Oligo Probe Name Number Reagent Oligo Sequence Length AKR1C1.1 BC040210 Forward GTGTGTGAAGCTGAATGATGG 21 BC040210 Reverse CTCTGCAGGCGCATAGGT 18 BC040210 Probe CCAAATCCCAGGACAGGCATGAAG 24 AKR1C2.1 NM_001354 Forward TGCCAGCTCATTGCTCTTAT 20 NM_001354 Reverse TCTGTCACTGGCCTGGTTAG 20 NM_001354 Probe CAAATGTTTCTTCCTCCCTCACAGGC 26 AKR1C3.1 NM_003739 Forward GCTTTGCCTGATGTCTACCAGAA 23 NM_003739 Reverse GTCCAGTCACCGGCATAGAGA 21 NM_003739 Probe TGCGTCACCATCCACACACAGGG 23 ATP5A1.1 NM_004046 Forward GATGCTGCCACTCAACAACT 20 NM_004046 Reverse TGTCCTTGCTTCAGCAACTC 20 NM_004046 Probe AGTTAGACGCACGCCACGACTCAA 24 B-actin.2 NM_001101 Forward CAGCAGATGTGGATCAGCAAG 21 NM_001101 Reverse GCATTTGCGGTGGACGAT 18 NM_001101 Probe AGGAGTATGACGAGTCCGGCCCC 23 Bcl2.1 NM_000633 Probe TGTACGGCCCCAGCATGCGG 20 NM_000633 Forward CTGGGATGCCTTTGTGGAA 19 NM_000633 Reverse CAGAGACAGCCAGGAGAAATCA 22 Bcl2.2 NM_000633 Forward CAGATGGACCTAGTACCCACTGAGA 25 NM_000633 Reverse CCTATGATTTAAGGGCATTTTTCC 24 NM_000633 Probe TTCCACGCCGAAGGACAGCGAT 22 Bcl2 intron 1 NM_000633 Forward GCATCATTTGTTGGGTATGGAGTT 24 50 kb.1 int1-50 kb NM_000633 Reverse TCTATGGAGGCCAATATTTGATTCT 25 int1-50 kb NM_000633 Probe AGCCAGTGTCCCTCAACCCAACTTCTG 27 int1-50 kb Bcl2 intron 1 NM_000633 Forward GGGCAGTGGCCTGATGAA 18 50 kb.2 int1-50 kb NM_000633 Reverse ATGGCAAAACTGTGTCTTTCCTTAT 25 int1-50 kb NM_000633 Probe CTTTTCTTCATTTTTGCT 18 int1-50 kb Bcl2 intron 1 NM_000633 Forward GTCACTTTTATCTCACAGCATCACAA 26 100 kb.1 int1-100 kb NM_000633 Reverse GCATTGGATCTTGGTGTCTTGA 22 int1-100 kb NM_000633 Probe AGGAACATCTGACAGCACTTGCCAGGTT 28 int1-100 kb Bcl2 intron 1 NM_000633 Forward GGAGAAGTAGCCAGCCCATTTAA 23 150 kb.2 int1-150 kb NM_000633 Reverse TGTCCCTGGCGCGTTTAG 18 int1-150 kb NM_000633 Probe ATGTCAGCAAAGATTCCAGT 20 int1-150 kb Bcl2 intron1 NM_000633 Forward CTAGCCACCCCCAAGAGAAAC 21 3′.1 int1-3 NM_000633 Reverse TGCCAACCTCTAAGGTCAAGGT 22 int1-3 NM_000633 Probe CCTGACAGCTCCCTTTCCCCAGGA 24 int1-3 Bcl2-beta.1 NM_000657 Forward TGGGTAGGTGCACTTGGTGAT 21 NM_000657 Reverse ACTCCAACCCCCGCATCT 18 NM_000657 Probe ACCTGTGGCCTCAGCCCAGACTCA 24 CAT.1 NM_001752 Forward ATCCATTCGATCTCACCAAGGT 22 NM_001752 Reverse TCCGGTTTAAGACCAGTTTACCA 23 NM_001752 Probe TGGCCTCACAAGGACTACCCTCTCATCC 28 CD68.2 NM_001251 Forward TGGTTCCCAGCCCTGTGT 18 NM_001251 Reverse CTCCTCCACCCTGGGTTGT 19 NM_001251 Probe CTCCAAGCCCAGATTCAGATTCGAGTCA 28 CDH1.3 NM_004360 Forward TGAGTGTCCCCCGGTATCTTC 21 NM_004360 Reverse CAGCCGCTTTCAGATTTTCAT 21 NM_004360 Probe TGCCAATCCCGATGAAATTGGAAATTT 27 CEGP1.2 NM_020974 Forward TGACAATCAGCACACCTGCAT 21 NM_020974 Reverse TGTGACTACAGCCGTGATCCTTA 23 NM_020974 Probe CAGGCCCTCTTCCGAGCGGT 20 CEGP1.6 NM_020974 Forward GCTGCATTTTATGTCCAAATGG 22 NM_020974 Reverse TGGTCTTGGGCATGGTTCA 19 NM_020974 Probe ATTTGTCCTTCCTCATTTTG 20 CEGP1 intron NM_020974 Forward TCCCCTTGCCTTTGGAGAA 19 4.1 NM_020974 Reverse AAAGGCCTGGAGGCATCAA 19 NM_020974 Probe CAGCCCAAATCCT 13 CEGP1 intron NM_020974 Forward CTTAATGGTGTTTAGCACAGATGCA 25 5.1 NM_020974 Reverse CCACTGTAGCATGCGAAGCA 20 NM_020974 Probe CAAATGCACAGGAAAC 16 COMT.1 NM_000754 Forward CCTTATCGGCTGGAACGAGTT 21 NM_000754 Reverse CTCCTTGGTGTCACCCATGAG 21 NM_000754 Probe CCTGCAGCCCATCCACAACCT 21 COX8.1 NM_004074 Forward CGTTCTGTCCCTCACACTGTGA 22 NM_004074 Reverse CAAATGCAGTAACATGACCAGGAT 24 NM_004074 Probe TGACCAGCCCCACCGGCC 18 CRYZ.1 NM_001889 Forward AAGTCCTGAAATTGCGATCA 20 NM_001889 Reverse CACATGCATGGACCTTGATT 20 NM_001889 Probe CCGATTCCAAAAGACCATCAGGTTCT 26 CTSL2.1 NM_001333 Forward TGTCTCACTGAGCGAGCAGAA 21 NM_001333 Reverse ACCATTGCAGCCCTGATTG 19 NM_001333 Probe CTTGAGGACGCGAACAGTCCACCA 24 CTSL2.10 NM_001333 Forward TCAGAGGCTTGTTTGCTGAG 20 NM_001333 Reverse AGGACGAGCGAAAGATTCAT 20 NM_001333 Probe CGACGGCTGCTGGTTTTGAAAC 22 CYP.1 NM_006347 Forward TGGACTTCTAGTGATGAGAAAGATTGA 27 NM_006347 Reverse CACTGCGAGATCACCACAGGTA 22 NM_006347 Probe TTCCCACAGGCCCCAACAATAAGCC 25 CYP17A1.1 NM_000102 Forward CCGGAGTGACTCTATCACCA 20 NM_000102 Reverse GCCAGCATTGCCATTATCT 19 NM_000102 Probe TGGACACACTGATGCAAGCCAAGA 24 CYP19A1.1 NM_000103 Forward TCCTTATAGGTACTTTCAGCCATTTG 26 NM_000103 Reverse CACCATGGCGATGTACTTTCC 21 NM_000103 Probe CACAGCCACGGGGCCCAAA 19 CYP1A1.2 NM_000499 Forward AATAATTTCGGGGAGGTGGT 20 NM_000499 Reverse GGTTGGGTAGGTAGCGAAGA 20 NM_000499 Probe TGGCTCTGGAAACCCAGCTGACTT 24 CYP1B1.3 NM_000104 Forward CCAGCTTTGTGCCTGTCACTAT 22 NM_000104 Reverse GGGAATGTGGTAGCCCAAGA 20 NM_000104 Probe CTCATGCCACCACTGCCAACACCTC 25 CYP4Z1.1 NM_178134 Forward GCCTTACACCACGATGTGCAT 21 NM_178134 Reverse GTCGAGTAACCGGGATATGTTTACTAC 27 NM_178134 Probe AAGGAATGCCTCCGCCTCTACGCAC 25 EPHX1.2 NM_000120 Forward ACCGTAGGCTCTGCTCTGAA 20 NM_000120 Reverse TGGTCCAGGTGGAAAACTTC 20 NM_000120 Probe AGGCAGCCAGACCCACAGGA 20 EstR1.1 NM_000125 Forward CGTGGTGCCCCTCTATGAC 19 NM_000125 Reverse GGCTAGTGGGCGCATGTAG 19 NM_000125 Probe CTGGAGATGCTGGACGCCC 19 FOXM1.1 NM_021953 Forward CCACCCCGAGCAAATCTGT 19 NM_021953 Reverse AAATCCAGTCCCCCTACTTTGG 22 NM_021953 Probe CCTGAATCCTGGAGGCTCACGCC 23 FOXM1.3 NM_021953 Forward TGCCCAGATGTGCGCTATTA 20 NM_021953 Reverse TCAATGCCAGTCTCCCTGGTA 21 NM_021953 Probe ATGTTTCTCTGATAATGTCC 20 FOXM1 intron NM_021953 Forward TGGACAGAGACAAGATGTGATGTG 24 5.1 NM_021953 Reverse GCTGGCACCTAGACAAAACATG 22 NM_021953 Probe CCATAGGGACCCTTC 15 FOXM1 intron NM_021953 Forward GGTGTCCTATTTTCCTCTGAAGAGA 25 7.1 NM_021953 Reverse TGCAAGCTGAAGGTCCAACAT 21 NM_021953 Probe TTCTGGCCAATTAAG 15 GAPDH.1 NM_002046 Forward ATTCCACCCATGGCAAATTC 20 NM_002046 Reverse GATGGGATTTCCATTGATGACA 22 NM_002046 Probe CCGTTCTCAGCCTTGACGGTGC 22 GCLC.3 NM_001498 Forward CTGTTGCAGGAAGGCATTGA 20 NM_001498 Reverse GTCAGTGGGTCTCTAATAAAGAGATGAG 28 NM_001498 Probe CATCTCCTGGCCCAGCATGTT 21 GCLM.2 NM_002061 Forward TGTAGAATCAAACTCTTCATCATCAACTAG 30 NM_002061 Reverse CACAGAATCCAGCTGTGCAACT 22 NM_002061 Probe TGCAGTTGACATGGCCTGTTCAGTCC 26 GPX1.2 NM_000581 Forward GCTTATGACCGACCCCAA 18 NM_000581 Reverse AAAGTTCCAGGCAACATCGT 20 NM_000581 Probe CTCATCACCTGGTCTCCGGTGTGT 24 GPX2.2 NM_002083 Forward CACACAGATCTCCTACTCCATCCA 24 NM_002083 Reverse GGTCCAGCAGTGTCTCCTGAA 21 NM_002083 Probe CATGCTGCATCCTAAGGCTCCTCAGG 26 GSTM1.1 NM_000561 Reverse GGCCCAGCTTGAATTTTTCA 20 NM_000561 Forward AAGCTATGAGGAAAAGAAGTACACGAT 27 NM_000561 Probe TCAGCCACTGGCTTCTGTCATAATCAGGAG 30 GSTM1 NM_146421 Forward CCATGGTTTGCAGGAAACAA 20 var2.1 NM_146421 Reverse AGAACACAGGTCTTGGGAGGAA 22 NM_146421 Probe ATCTCTGCCTACATGAAGTCCAGCC 25 GSTM1 intron NM_000561 Forward AACGGGTACGTGCAGTGTAAACT 23 1.1 NM_000561 Reverse GCAGGTCGCGTCAGAGATG 19 NM_000561 Probe CCCTGAC1TTGTCTGCACCAGGGAAG 26 GSTM1 intron NM_000561 Forward TCTGTGTCCACCTGCATTCG 20 3.1 NM_000561 Reverse CTGCTCATGGCAGGACTGAA 20 NM_000561 Probe TCATGTGACAGTATTCTTA 19 GSTM1 intron NM_000561 Forward CGACTCCAATGTCATGTCAACA 22 5.1 int5 NM_000561 Reverse ACCCTGGGATGCCTGGAT 18 int5 NM_000561 Probe AGAGGCAATTCCCACCAACCTTAGGACA 28 int5 GSTM1 intron NM_000561 Forward GGCAATTCCCACCAACCTTA 20 5.2 int5 NM_000561 Reverse AAACTTTACCATACAGGAACTGAATTTCT 29 int5 NM_000561 Probe ACACGATCCAGGCATCCCAGGG 22 int5 GSTM1 intron NM_000561 Forward ATGGCACCCTCGAATTGC 18 5.3 int5 NM_000561 Reverse TGCATGTCAATGACAGCACTCA 22 int5 NM_000561 Probe TCTTCTCCTCAACAGTTTT 19 int5 GSTM1 intron NM_000561 Forward GCCTCCCTGTGGAAAAGGA 19 7.2 int7 NM_000561 Reverse TCACACCAGGCCCTGTCA 18 int7 NM_000561 Probe TCCTTGACTGCACAAACAG 19 int7 GSTM2 gene.1 NM_000848 Forward GCAGGAACGAGAGGAGGAGAT 21 gene NM_000848 Reverse CAGCTCGGGTCAGAGATGGA 20 gene NM_000548 Probe CTCCCCTTGTGCAGAGTCGTCACAAA 26 gene GSTM2 gene.4 NM_000545 Forward CTGGGCTGTGAGGCTGAGA 19 gene NM_000848 Reverse GCGAATCTGCTCCTTTTCTGA 21 gene NM_000848 Probe CCCGCCTACCCTCGTAAAGCAGATTCA 27 gene GSTM3.2 NM_000849 Forward CAATGCCATCTTGCGCTACAT 21 NM_000849 Reverse GTCCACTCGAATCTTTTCTTCTTCA 25 NM_000849 Probe CTCGCAAGCACAACATGTGTGGTGAGA 27 GSTM3.5 NM_000849 Forward CCAGAAGCCAAGGATCTCTCTAGT 24 NM_000849 Reverse TATTCCTCCTGACATCACTGGGTAT 25 NM_000849 Probe TGCCATTTGGGCCCTCTGACCAT 23 GSTM3.6 NM_000849 Forward TCACAGTTTCCCTAGTCCTCGAA 23 NM_000849 Reverse CGAATATCCCAGTACCCGAGAA 22 NM_000849 Probe CCCGTCACCATGTCGTGCGAGTC 23 GSTM4.1 NM_000850 Forward CGGACCTTGCTCCCTGAAC 19 NM_000850 Reverse CGGAGCAGGTTGCTGGAT 18 NM_000850 Probe AGTAAGATCCACCGCCACCTCCGAG 25 GSTM5.1 NM_000851 Forward TCCCTGAGGCTCCCTTGACT 20 NM_000851 Reverse GGCTGTGGACAACAGAAGACAA 22 NM_000851 Probe CCACCCACAATTCGAGCACAGTCCT 25 GSTM5.2 NM_000851 Forward GAAAGGTGCTCTGTGCCAAGT 21 NM_000851 Reverse CCTAGCCCCTCTTTGAACCAT 21 NM_000851 Probe ATTCGCGCTCCTGTAGGCCGTCTAGAA 27 GSTp.3 NM_000852 Forward GAGACCCTGCTGTCCCAGAA 20 NM_000852 Reverse GGTTGTAGTCAGCGAAGGAGATC 23 NM_000852 Probe TCCCACAATGAAGGTCTTGCCTCCCT 26 GSTT1.3 NM_000853 Forward CACCATCCCCACCCTGTCT 19 NM_000853 Reverse GGCCTCAGTGTGCATCATTCT 21 NM_000853 Probe CACAGCCGCCTGAAAGCCACAAT 23 GUS.1 NM_000181 Forward CCCACTCAGTAGCCAAGTCA 20 NM_000181 Reverse CACGCAGGTGGTATCAGTCT 20 NM_000181 Probe TCAAGTWCGGGCTG1TTTCCAAACA 27 HOXB13.1 NM_006361 Forward CGTGCCTTATGGTTACTTTGG 21 NM_006361 Reverse CACAGGGTTTCAGCGAGC 18 NM_006361 Probe ACACTCGGCAGGAGTAGTACCCGC 24 HSD17B1.1 NM_000413 Forward CTGGACCGCACGGACATC 18 NM_000413 Reverse CGCCTCGCGAAAGACTTG 18 NM_000413 Probe ACCGCTTCTACCAATACCTCGCCCA 25 HSD17B2.1 NM_002153 Forward GCTTTCCAAGTGGGGAATTA 20 NM_002153 Reverse TGCCTGCGATATTTGTTAGG 20 NM_002153 Probe AGTTGCTTCCATCCAACCTGGAGG 24 HSD17B4.1 NM_000414 Forward TTGTCCTTTGGCTTTTGTCAC 20 NM_000414 Reverse CAATCCATCCTGCTCCAAC 19 NM_000414 Probe CAAACAAGCCACCATTCTCCTCACA 25 IL17RB.2 NM_018725 Forward ACCCTCTGGTGGTAAATGGA 20 NM_018725 Reverse GGCCCCAATGAAATAGACTG 20 NM_018725 Probe TCGGCTTCCCTGTAGAGCTGAACA 24 IMMT.1 NM_006839 Forward CTGCCTATGCCAGACTCAGA 20 NM_006839 Reverse GCTTTTCTGGCTTCCTCTTC 20 NM_006839 Probe CAACTGCATGGCTCTGAACAGCCT 24 Ki-67.2 NM_002417 Forward CGGACTTTGGGTGCGACTT 19 NM_002417 Reverse TTACAACTCTTCCACTGGGACGAT 24 NM_002417 Probe CCACTTGTCGAACCACCGCTCGT 23 LIPA.1 NM_000235 Forward CCAGTTGTCTTCCTGCAACA 20 NM_000235 Reverse CTGTTGGCAAGGTTTGTGAC 20 NM_000235 Probe CCAGTTACTAGAATCTGCCAGCAAGCCA 28 MDH2.1 NM_005918 Forward CCAACACCTTTGTTGCAGAG 20 NM_005918 Reverse CAATGACAGGGACGTTGACT 20 NM_005918 Probe CGAGCTGGATCCAAACCCTTCAG 23 mGST1.2 NM_020300 Forward ACGGATCTACCACACCATTGC 21 NM_020300 Reverse TCCATATCCAACAAAAAAACTCAAAG 26 NM_020300 Probe TTTGACACCCCTTCCCCAGCCA 22 MGST3.1 NM_004528 Forward AGCTGTTGGAGGTGTTTACCA 21 NM_004528 Reverse TCGTCCAACAATCCAGGC 18 NM_004528 Probe AAGCCCAGGCCAGAAGCTATACGC 24 MMTV-like AF346816 Forward CCATACGTGCTGCTACCTGT 20 env. 3 AF346816 Reverse CCTAAAAGGTTTGAATGGCAGA 21 AF346816 Probe TCATCAAACCATGGTTCATCACCAATATC 29 MPV17.1 NM_002437 Forward CCAATGTGTTGCTGTTATCTGGAA 24 NM_002437 Reverse ATGGAGTGAGGCAGGCTTAGAG 22 NM_002437 Probe TCCTACCTGTCCTGGAAGGCACATCG 26 MVP.1 NM_017458 Forward ACGAGAACGAGGGCATCTATGT 22 NM_017458 Reverse GCATGTAGGTGCTTCCAATCAC 22 NM_017458 Probe CGCACCTTTCCGGTCTTGACATCCT 25 NAT1.1 NM_000662 Forward TGGTTTTGAGACCACGATGT 20 NM_000662 Reverse TGAATCATGCCAGTGCTGTA 20 NM_000662 Probe TGGAGTGCTGTAAACATACCCTCCCA 26 NAT2.1 NM_000015 Forward TAACTGACATTCTTGAGCACCAGAT 25 NM_000015 Reverse ATGGCTTGCCCACAATGC 18 NM_000015 Probe CGGGCTGTTCCCTTTGAGAACCTTAACA 28 NCOA2.1 NM_006540 Forward AGTGACCTCCGTGCCTACGT 20 NM_006540 Reverse CTCCCCTCAGAGCAGGATCA 20 NM_006540 Probe CCTCCATGGGTCCCGAGCAGG 21 NDUFA7.1 NM_005001 Forward GCAGCTACGCTACCAGGAG 19 NM_005001 Reverse GGAGAGCTTGTGGCTAGGAC 20 NM_005001 Probe TCTCCAAGCGAACTCAGCCTCCTC 24 NQO1.1 NM_000903 Forward CAGCAGACGCCCGAATTC 18 NM_000903 Reverse TGGTGTCTCATCCCAAATATTCTC 24 NM_000903 Probe AGGCGTTTCTTCCATCCTTCCAGGATT 27 NQO2.1 NM_000904 Forward AGCGCTCCTTTCCGTAACC 19 NM_000904 Reverse TCCATTGACTCCTGTCTTTCGTGTA 24 NM_000904 Probe ATCTCGGCCGTGCCTCCCG 19 P53.2 NM_000546 Forward CTTTGAACCCTTGCTTGCAA 20 NM_000546 Reverse CCCGGGACAAAGCAAATG 18 NM_000546 Probe AAGTCCTGGGTGCTTCTGACGCACA 25 PAI1.3 NM_000602 Forward CCGCAACGTGGTTTTCTCA 19 NM_000602 Reverse TGCTGGGTTTCTCCTCCTGTT 21 NM_000602 Probe CTCGGTGTTGGCCATGCTCCAG 22 PR.6 NM_000926 Forward GCATCAGGCTGTCATTATGG 20 NM_000926 Reverse AGTAGTTGTGCTGCCCTTCC 20 NM_000926 Probe TGTCCTTACCTGTGGGAGCTGTAAGGTC 28 PR.12 NM_000926 Forward GTTCCATCCCAAAGAACCTG 20 NM_000926 Reverse GAAACTCTGGAGTTGGCATTT 21 NM_000926 Probe CCACCCGTTATTCTGAATGCTACTCTCA 28 PRAME.3 NM_006115 Forward TCTCCATATCTGCCTTGCAGAGT 23 NM_006115 Reverse GCACGTGGGTCAGATTGCT 19 NM_006115 Probe TCCTGCAGCACCTCATCGGGCT 22 PRAME.4 NM_006115 Forward CCACTGCTCCCAGCTTACAAC 21 NM_006115 Reverse CTGCAAGGCAGATATGGAGATG 22 NM_006115 Probe AATTCCCGTAGAAGCTTAA 19 PRAME intron NM_006115 Forward ATCAGGCACAGAGATAGAGGTGACT 25 5.1 NM_006115 Reverse TCTTTCAACTCGGGCTTCCTT 21 NM_006115 Probe CCCAGGCAGTGGCA 14 PRDX2.1 NM_005809 Forward GGTGTCCTTCGCCAGATCAC 20 NM_005809 Reverse CAGCCGCAGAGCCTCATC 18 NM_005809 Probe TTATGATTTGCCTGTGGGACGCTCC 26 PRDX3.1 NM_006793 Forward TGACCCCAATGGAGTCATCA 20 NM_006793 Reverse CCAAGCGGAGGGTTTCTTC 19 NM_006793 Probe CATTTGAGCGTCAACGATCTCCCAGTG 27 PRDX4.1 NM_006406 Forward TTACCCATTTGGCCTGGATTAA 22 NM_006406 Reverse CTGAAAGAAGTGGAATCCTTATTGG 25 NM_006406 Probe CCAAGTCCTCCTTGTCTTCGAGGGGT 26 PRDX6.1 NM_004905 Forward CTGTGAGCCAGAGGATGTCA 20 NM_004905 Reverse TGTGATGACACCAGGATGTG 20 NM_004905 Probe CTGCCAATTGTGTTTTCCTGCAGC 24 RPLPO.2 NM_001002 Forward CCATTCTATCATCAACGGGTACAA 24 NM_001002 Reverse TCAGCAAGTGGGAAGGTGTAATC 23 NM_001002 Probe TCTCCACAGACAAGGCCAGGACTCG 25 SC5DL.1 NM_006918 Forward CGCCTACATAAACCTCACCA 20 NM_006918 Reverse CCATCAATAGGGTGAAAAGCA 21 NM_006918 Probe TGGAAGATTCCTACTCCATTTGCAAGTCA 29 SOD1.1 NM_000454 Forward TGAAGAGAGGCATGTTGGAG 20 NM_000454 Reverse AATAGACACATCGGCCACAC 20 NM_000454 Probe TTTGTCAGCAGTCACATTGCCCAA 24 SOD2.1 NM_000636 Forward GCTTGTCCAAATCAGGATCCA 21 NM_000636 Reverse AGCGTGCTCCCACACATCA 19 NM_000636 Probe AACAACAGGCCTTATTCCACTGCTGGG 27 SOD3.1 NM_003102 Forward CCATAAGCCCTGAGACTCCC 20 NM_003102 Reverse TAGGAGGAACCTGAAGGCG 19 NM_003102 Probe TTGACCTGACGATCTTCCCCCTTC 24 SRD5A2.1 NM_000348 Forward GTAGGTCTCCTGGCGTTCTG 20 NM_000348 Reverse TCCCTGGAAGGGTAGGAGTAA 21 NM_000348 Probe AGACACCACTCAGAATCCCCAGGC 24 STK15.2 NM_003600 Forward CATCTTCCAGGAGGACCACT 20 NM_003600 Reverse TCCGACCTTCAATCATTTTCA 20 NM_003600 Probe CTCTGTGGCACCCTGGACTACCTG 24 STK15.8 NM_003600 Forward GCCCCCTGAAATGATTGAAG 20 NM_003600 Reverse TCCAAGGCTCCAGAGATCCA 20 NM_003600 Probe TTCTCATCATGCATCCGA 18 STK15 intron NM_003600 Forward CATTCACATTTATAAACCCACATGGA 26 2.1 NM_003600 Reverse AATCCAAAGTAAAGGCGGAAAGA 23 NM_003600 Probe TGGTCTTGTCGGGAAT 16 STK15 intron NM_003600 Forward GCGAGGAATGAACCCACAGA 20 4.1 NM_003600 Reverse GCATGAGAACCAGTGGATTTAGACT 25 NM_003600 Probe CGCTAAAAGCAAAAGA 16 SULT1E1.1 NM_005420 Forward ATGGTGGCTGGTCATCCAA 19 NM_005420 Reverse ATAAGGAACCTGTCCTTGCATGAA 24 NM_005420 Probe TCTCCACAAACTCTGGAAAGGATCCAGGA 30 SULT4A1.1 NM_014351 Forward CACCTGCCCTACCGCTTTC 19 NM_014351 Reverse GGGTTGCGAGCCATATAGATG 21 NM_014351 Probe CCTCTGACCTCCACAATGGAGACTCCA 27 SURV.2 NM_001168 Forward TGTTTTGATTCCCGGGCTTA 20 NM_001168 Reverse CAAAGCTGTCAGCTCTAGCAAAAG 24 NM_001168 Probe TGCCTTCTTCCTCCCTCACTTCTCACCT 28 TBP.1 NM_003194 Forward GCCCGAAACGCCGAATATA 19 NM_003194 Reverse CGTGGCTCTCTTATCCTCATGAT 23 NM_003194 Probe TACCGCAGCAAACCGCTTGGG 21 TFRC.3 NM_003234 Forward GCCAACTGCTTTCATTTGTG 20 NM_003234 Reverse ACTCAGGCCCATTTCCTTTA 20 NM_003234 Probe AGGGATCTGAACCAATACAGAGCAGACA 28 TST.1 NM_003312 Forward GGAGCCGGATGCAGTAGGA 19 NM_003312 Reverse AAGTCCATGAAAGGCATGTTGA 22 NM_003312 Probe ACCACGGATATGGCCCGAGTCCA 23 UGT1A3.1 NM_019093 Forward GATGCCCTTGTTTGGTGATCA 21 NM_019093 Reverse AGGGTCACTCCAGCTCCCTTA 21 NM_019093 Probe TCTCCATGCGCTTTGCATTGTCCA 24 UGT2B7.2 NM_001074 Forward CAATGGCATCTACGAGGCA 19 NM_001074 Reverse CAGGTTGATCGGCAAACA 18 NM_001074 Probe AATCCCCACCATAGGGATCCCATG 24 upa.3 NM_002658 Forward GTGGATGTGCCCTGAAGGA 19 NM_002658 Reverse CTGCGGATCCAGGGTAAGAA 20 NM_002658 Probe AAGCCAGGCGTCTACACGAGAGTCTCAC 28 VDAC1.1 NM_003374 Forward GCTGCGACATGGATTTCGA 19 NM_003374 Reverse CCAGCCCTCGTAACCTAGCA 20 NM_003374 Probe TTGCTGGGCCTTCCATCCGG 20 VDAC2.1 NM_003375 Forward ACCCACGGACAGACTTGC 18 NM_003375 Reverse AGCTTTGCCAAGGTCAGC 18 NM_003375 Probe CGCGTCCAATGTGTATTCCTCCAT 24 XPC.1 NM_004628 Forward GATACATCGTCTGCGAGGAA 20 NM_004628 Reverse CTTTCAATGACTGCCTGCTC 20 NM_004628 Probe TTCAAAGACGTGCTCCTGACTGCC 24

TABLE 7 Amplicon Accession Name Number Amplicon Sequence AKR1C1.1 BC040210 AGATGAGAGCAGCCTGAACTTACACTGTGAAAATGCCCTGGAGAAATGCAGAGATGCAGGTTTAATGA AGTCCATCA AKR1C2.1 NM_001354 TGCCAGCTCATTGCTCTTATAGCCTGTGAGGGAGGAAGAAACATTTGCTAACCAGGCCAGTGACAGA AKR1C3.1 NM_003739 GCTTTGCCTGATGTCTACCAGAAGCCCTGTGTGTGGATGGTGACGCAGAGGACGTCTCTATGCCGGTG ACTGGAC ATP5A1.1 NM_004046 GATGCTGCCACTCAACAACTTTTGAGTCGTGGCGTGCGTCTAACTGAGTTGCTGAAGCAAGGACA B-actin.2 NM_001101 CAGCAGATGTGGATCAGCAAGCAGGAGTATGACGAGTCCGGCCCCTCCATCGTCCACCGCAAATGC Bcl2.1 NM_000633 CTGGGATGCCTTTGTGGAACTGTACGGCCCCAGCATGCGGCCTCTGTTTGATTTCTCCTGGCTGTCTC TG Bcl2.2 NM_000633 CAGATGGACCTAGTACCCACTGAGATTTCCACGCCGAAGGACAGCGATGGGAAAAATGCCCTTAAATC ATAGG Bcl2 intron NM_000633int1-50 kb GCATCATTTGTTGGGTATGGAGTTGCAGAAGTTGGGTTGAGGGACACTGGCTTCTAGAATCAAATATT 1 50 kb.1 GGCCTCCATAGA Bcl2 intron NM_000633int1-50 kb GGGCAGTGGCCTGATGAAAAGCAAAAATGAAGAAAAGAATAAGGAAAGACACAGTTTTGCCAT 1 50 kb.2 Bcl2 intron NM_000633int1-100 kb GTCACTTTTATCTCACAGCATCACAAGGAGGAACATCTGACAGCACTTGCCAGGTTATCAAGACACCA 1 100 kb.1 AGATCCAATGC Bcl2 intron NM_000633int1-150 kb GGAGAAGTAGCCAGCCCATTTAAAATGTCAGCAAAGATTCCAGTTGTCTAAACGCGCCAGGGACA 1 150 kb.2 Bcl2 intron NM_000633int1-3 CTAGCCACCCCCAAGAGAAACCCCCTGACAGCTCCCTTTCCCCAGGAGAACCTTGACCTTAGAGGTTG 1 3′.1 GCA Bcl2-beta.1 NM_000657 TGGGTAGGTGCACTTGGTGATGTGAGTCTGGGCTGAGGCCACAGGTCCGAGATGCGGGGGTTGGAGT CAT.1 NM_001752 ATCCATTCGATCTCACCAAGGTTTGGCCTCACAAGGACTACCCTCTCATCCCAGUGGTAAACTGGTCT TAAACCGGA CD68.2 NM_001251 TGGTTCCCAGCCCTGTGTCCACCTCCAAGCCCAGATTCAGATTCGAGTCATGTACACAACCCAGGGTG GAGGAG CDH1.3 NM_004360 TGAGTGTCCCCCGGTATCTTCCCCGCCCTGCCAATCCCGATGAAATTGGAAATTTTATTGATGAAAAT CTGAAAGCGGCTG CEGP1.2 NM_020974 TGACAATCAGCACACCTGCATTCACCGCTCGGAAGAGGGCCTGAGCTGCATGAATAAGGATCACGGCT GTAGTCACA CEGP1.6 NM_020974 GCTGCATTTTATGTCCAAATGGAACCTTCCAAAATGAGGAAGGACAAATGACTTGTGAACCATGCCCA AGACCA CEGP1 intron NM_020974int4 TCCCCTTGCCTTTGGAGAACAGCCCAAATCCTTTGATGCCTCCAGGCCTTT 4.1 CEGP1 intron NM_020974int5 CTTAATGGTGTTTAGCACAGATGCAGGCTGTTTCCTGTGCATTTGCCCCCCCAGCAGGCCCTGTGCTG 5.1 CTTCGCATGCTACAGTGG COMT.1 NM_000754 CCTTATCGGCTGGAACGAGTTCATCCTGCAGCCCATCCACAACCTGCTCATGGGTGACACCAAGGAG COX8.1 NM_004074 CGTTCTGTCCCTCACACTGTGACCTGACCAGCCCCACCGGCCCATCCTGGTCATGTTACTGCATTTG CRYZ.1 NM_001889 AAGTCCTGAAATTGCGATCAGATATTGCAGTACCGATTCCAAAAGACCATCAGGTTCTAATCAAGGTC CATGCATGTG CTSL2.1 NM_001333 TGTCTCACTGAGCGAGCAGAATCTGGTGGACTGTTCGCGTCCTCAAGGCAATCAGGGCTGCAATGGT CTSL2.10 NM_001333 TCAGAGGCTTGTTTGCTGAGGGTGCCTGCGCAGCTGCGACGGCTGCTGGTTTTGAAACATGAATCTTT CGCTCGTCCT CYP.1 NM_006347 TGGACTTCTAGTGATGAGAAAGATTGAGAATGTTCCCACAGGCCCCAACAATAAGCCCAAGCTACCTG TGGTGATCTCGCAGTG CYP17A1.1 NM_000102 CCGGAGTGACTCTATCACCAACATGCTGGACACACTGATGCAAGCCAAGATGAACTCAGATAATGGCA ATGCTGGC CYP19A1.1 NM_000103 TCCTTATAGGTACTTTCAGCCATTTGGCTTTGGGCCCCGTGGCTGTGCAGGAAAGTACATCGCCATGG TG CYP1A1.2 NM_000499 AATAATTTCGGGGAGGTGGTTGGCTCTGGAAACCCAGCTGACTTCATCCCTATTCTTCGCTACCTACC CAACC CYP1B1.3 NM_000104 CCAGCTTTGTGCCTGTCACTATTCCTCATGCCACCACTGCCAACACCTCTGTCTTGGGCTACCACATT CCC CYP4Z1.1 NM_178134 GCCTTACACCACGATGTGCATCAAGGAATGCCTCCGCCTCTACGCACCGGTAGTAAACATATCCCGGT TACTCGAC EPHX1.2 NM_000120 ACCGTAGGCTCTGCTCTGAATGACTCTCCTGTGGGTCTGGCTGCCTATATTCTAGAGAAGTTTTCCAC CTGGACCA EstR1.1 NM_000125 CGTGGTGCCCCTCTATGACCTGCTGCTGGAGATGCTGGACGCCCACCGCCTACATGCGCCCACTAGCC FOXM1.1 NM_021953 CCACCCCGAGCAAATCTGTCCTCCCCAGAACCCCTGAATCCTGGAGGCTCACGCCCCCAGCCAAAGTA GGGGGACTGGATTT FOXM1.3 NM_021953 TGCCCAGATGTGCGCTATTAGATGTTTCTCTGATAATGTCCCCAATCATACCAGGGAGACTGGCATTG A FOXM1 intron NM_021953 int5 TGGACAGAGACAAGATGTGATGTGGGGAAGGGTCCCTATGGCCATGTTTTGTCTAGGTGCCAGC 5.1 FOXM1 intron NM_021953 int7 GGTGTCCTATTTTCCTCTGAAGAGAGATTCTGGCCAATTAAGAATGTTGGACCTTCAGCTTGCA 7.1 GAPDH.1 NM_002046 ATTCCACCCATGGCAAATTCCATGGCACCGTCAAGGCTGAGAACGGGAAGCTTGTCATCAATGGAAAT CCCATC GCLC.3 NM_001498 CTGTTGCAGGAAGGCATTGATCATCTCCTGGCCCAGCATGTTGCTCATCTCTTTATTAGAGACCCACT GAC GCLM.2 NM_002061 TGTAGAATCAAACTCTTCATCATCAACTAGAAGTGCAGTTGACATGGCCTGTTCAGTCCTTGGAGTTG CACAGCTGGATTCTGTG GPX1.2 NM_000581 GCTTATGACCGACCCCAAGCTCATCACCTGGTCTCCGGTGTGTCGCAACGATGTTGCCTGGAACTTT GPX2.2 NM_002083 CACACAGATCTCCTACTCCATCCAGTCCTGAGGAGCCTTAGGATGCAGCATGCCTTCAGGAGACACTG CTGGACC GSTM1.1 NM_000561 AAGCTATGAGGAAAAGAAGTACACGATGGGGGACGCTCCTGATTATGACAGAAGCCAGTGGCTGAATG AAAAATTCAAGCTGGGCC GSTM1 var2.1 NM_146421 CCATGGTTTGCAGGAAACAAGGGCTTGGAGAAGATCTCTGCCTACATGAAGTCCAGCCGCTTCCTCCC AAGACCTGTGTTCT GSTM1 intron NM_000561int1 AACGGGTACGTGCAGTGTAAACTGGGGGCTTCCCTGGTGCAGACAAAGTCAGGGACCCTCCATCTCTG 1.1 ACGCGACCTGC GSTM1 intron NM_000561int3 TCTGTGTCCACCTGCATTCGTTCATGTGACAGTATTCTTATTTCAGTCCTGCCATGAGCAG 3.1 GSTM1 intron NM_000561int5 CGACTCCAATGTCATGTCAACAAAAGCAGAGGCAATTCCCACCAACCTTAGGACACGATCCAGGCATC 5.1 CCAGGGT GSTM1 intron NM_000561int5 GGCAATTCCCACCAACCTTAGGACACGATCCAGGCATCCCAGGGTAGAAATTCAGTTCCTGTATGGTA 5.2 AAGTTT GSTM1 intron NM_000561int5 ATGGCACCCTCGAATTGCATCTTCTCCTCAACAGTTTTCTGAGTGCTGTCATTGACATGCA 5.3 GSTM1 intron NM_000561int7 GCCTCCCTGTGGAAAAGGAGACTGTTTGTGCAGTCAAGGAGTGACAGGGCCTGGTGTGA 7.2 GSTM2 gene.1 NM_000848gene GCAGGAACGAGAGGAGGAGATGGGGCTCCCCTTGTGCAGAGTCGTCACAAAGTCAGGGACCCTCCATC TCTGACCCGAGCTG GSTM2 gene.4 NM_000848gene CTGGGCTGTGAGGCTGAGAGTGAATCTGCTTTACGAGGGTAGGCGGGGAATCAGAAAAGGAGCAGATT CGC GSTM3.2 NM_000849 CAATGCCATCTTGCGCTACATCGCTCGCAAGCACAACATGTGTGGTGAGACTGAAGAAGAAAAGATTC GAGTGGAC GSTM3.5 NM_000849 CCAGAAGCCAAGGATCTCTCTAGTGATGGTCAGAGGGCCCAAATGGCAGGGATACCCAGTGATGTCAG GAGGAATA GSTM3.6 NM_000849 TCACAGTTTCCCTAGTCCTCGAAGGCTCGGAAGCCCGTCACCATGTCGTGCGAGTCGTCTATGGTTCT CGGGTACTGGGATATTCG GSTM4.1 NM_000850 CGGACCTTGCTCCCTGAACACTCGGAGGTGGCGGTGGATCTTACTCCTTCCAGCCAGTGAGGATCCAG CAACCTGCTCCG GSTM5.1 NM_000851 TCCCTGAGGCTCCCTTGACTCAGGACTGTGCTCGAATTGTGGGTGGTTTTTTGTCTTCTGTTGTCCAC AGCC GSTM5.2 NM_000851 GAAAGGTGCTCTGTGCCAAGTTCCTCACTCATTCGCGCTCCTGTAGGCCGTCTAGAACTGGCATGGTT CAAAGAGGGGCTAGG GSTp.3 NM_000852 GAGACCCTGCTGTCCCAGAACCAGGGAGGCAAGACCTTCATTGTGGGAGACCAGATCTCCTTCGCTGA CTACAACC GSTT1.3 NM_000853 CACCATCCCCACCCTGTCTTCCACAGCCGCCTGAAAGCCACAATGAGAATGATGCACACTGAGGCC GUS.1 NM_000181 CCCACTCAGTAGCCAAGTCACAATGTTTGGAAAACAGCCCGTTTACTTGAGCAAGACTGATACCACCT GCGTG HOXB13.1 NM_006361 CGTGCCTTATGGTTACTTTGGAGGCGGGTACTACTCCTGCCGAGTGTCCCGGAGCTCGCTGAAACCCT GTG HSD17B1.1 NM_000413 CTGGACCGCACGGACATCCACACCTTCCACCGCTTCTACCAATACCTCGCCCACAGCAAGCAAGTCTT TCGCGAGGCG HSD17B2.1 NM_002153 GCTTTCCAAGTGGGGAATTAAAGTTGCTTCCATCCAACCTGGAGGCTTCCTAACAAATATCGCAGGCA HSD17B4.1 NM_000414 TTGTCCTTTGGCTTTGTCACGAGAGTTGTGAGGAGAATGGTGGCTTGTTTGAGGTTGGAGCAGGATGG ATTG IL17RB.2 NM_018725 ACCCTCTGGTGGTAAATGGACATTTTCCTACATCGGCTTCCCTGTAGAGCTGAACACAGTCTATTTCA TTGGGGCC IMMT.1 NM_006839 CTGCCTATGCCAGACTCAGAGGAATCGAACAGGCTGTTCAGAGCCATGCAGTTGCTGAAGAGGAAGCC AGAAAAGC Ki-67.2 NM_002417 CGGACTTTGGGTGCGACTTGACGAGCGGTGGTTCGACAAGTGGCCTTGCGGGCCGGATCGTCCCAGTG GAAGAGTTGTAA LIPA.1 NM_000235 CCAGTTGTCTTCCTGCAACATGGCTTGCTGGCAGATTCTAGTAACTGGGTCACAAACCTTGCCAACAG MDH2.1 NM_005918 CCAACACCTTTGTTGCAGAGCTGAAGGGTTTGGATCCAGCTCGAGTCAACGTCCCTGTCATTG mGST1.2 NM_020300 ACGGATCTACCACACCATTGCATATTTGACACCCCTTCCCCAGCCAAATAGAGCTTTGAGTTTTTTTG TTGGATATGGA MGST3.1 NM_004528 AGCTGTTGGAGGTGTTTACCACCCGCGTATAGCTTCTGGCCTGGGCTTGGCCTGGATTGTTGGACGA MMTV-like AF346816 CCATACGTGCTGCTACCTGTAGATATTGGTGATGAACCATGGTTTGATGATTCTGCCATTCAAACCTT env.3 TAGG MPV17.1 NM_002437 CCAATGTGTTGCTGTTATCTGGAACTCCTACCTGTCCTGGAAGGCACATCGGCTCTAAGCCTGCCTCA CTCCAT MVP.1 NM_017458 ACGAGAACGAGGGCATCTATGTGCAGGATGTCAAGACCGGAAAGGTGCGCGCTGTGATTGGAAGCACC TACATGC NAT1.1 NM_000662 TGGTTTTGAGACCACGATGTTGGGAGGGTATGTTTACAGCACTCCAGCCAAAAAATACAGCACTGGCA TGATTCA NAT2.1 NM_000015 TAACTGACATTCTTGAGCACCAGATCCGGGCTGTTCCCTTTGAGAACCTTAACATGCATTGTGGGCAA GCCAT NCOA2.1 NM_006540 AGTGACCTCCGTGCCTACGTCAGGGCTGTCCTCCATGGGTCCCGAGCAGGTTAATGATCCTGCTCTGA GGGGAG NDUFA7.1 NM_005001 GCAGCTACGCTACCAGGAGATCTCCAAGCGAACTCAGCCTCCTCCCAAGCTCCCTGTGGGTCCTAGCC ACAAGCTCTCC NQO1.1 NM_000903 CAGCAGACGCCCGAATTCAAATCCTGGAAGGATGGAAGAAACGCCTGGAGAATATTTGGGATGAGACA CCA NQO2.1 NM_000904 AGCGCTCCTTTCCGTAACCACGGGAGGCACGGCCGAGATGTACACGAAGACAGGAGTCAATGGA P53.2 NM_000546 CTTTGAACCCTTGCTTGCAATAGGTGTGCGTCAGAAGCACCCAGGACTTCCATTTGCTTTGTCCCGGG PAI1.3 NM_000602 CCGCAACGTGGTTTTCTCACCCTATGGGGTGGCCTCGGTGTTGGCCATGCTCCAGCTGACAACAGGAG GAGAAACCCAGCA PR.6 NM_000926 GCATCAGGCTGTCATTATGGTGTCCTTACCTGTGGGAGCTGTAAGGTCTTCTTTAAGAGGGCAATGGA AGGGCAGCACAACTACT PR.12 NM_000926 GTTCCATCCCAAAGAACCTGCTATTGAGAGTAGCATTCAGAATAACGGGTGGAAATGCCAACTCCAGA GTTTC PRAME.3 NM_006115 TCTCCATATCTGCCTTGCAGAGTCTCCTGCAGCACCTCATCGGGCTGAGCAATCTGACCCACGTGC PRAME.4 NM_006115 CCACTGCTCCCAGCTTACAACCTTAAGCTTCTACGGGAATTCCATCTCCATATCTGCCTTGCAG PRAME intron NM_006115 ATCAGGCACAGAGATAGAGGTGACTGGGGCCCAGGCAGTGGCAGAAGGAAGCCCGAGTTGAAAGA 5.1 PRDX2.1 NM_005809 GGTGTCCTTCGCCAGATCACTGTTAATGATTTGCCTGTGGGACGCTCCGTGGATGAGGCTCTGCGGCT G PRDX3.1 NM_006793 TGACCCCAATGGAGTCATCAAGCATTTGAGCGTCAACGATCTCCCAGTGGGCCGAAGCGTGGAAGAAA CCCTCCGCTTGG PRDX4.1 NM_006406 TTACCCATTTGGCCTGGATTAATACCCCTCGAAGACAAGGAGGACTTGGGCCAATAAGGATTCCACTT CTTTCAG PRDX6.1 NM_004905 CTGTGAGCCAGAGGATGTCAGCTGCCAATTGTGTTTTCCTGCAGCAATTCCATAAACACATCCTGGTG TCATCACA RPLPO.2 NM_001002 CCATTCTATCATCAACGGGTACAAACGAGTCCTGGCCTTGTCTGTGGAGACGGATTACACCTTCCCAC TTGCTGA SC5DL.1 NM_006918 CGCCTACATAAACCTCACCATATTTGGAAGATTCCTACTCCATTTGCAAGTCATGCTTTTCACCCTAT TGATGG SOD1.1 NM_000454 TGAAGAGAGGCATGTTGGAGACTTGGGCAATGTGACTGCTGACAAAGATGGTGTGGCCGATGTGTCTA TT SOD2.1 NM_000636 GCTTGTCCAAATCAGGATCCACTGCAAGGAACAACAGGCCTTATTCCACTGCTGGGGATTGATGTGTG GGAGCACGCT SOD3.1 NM_003102 CCATAAGCCCTGAGACTCCCGCCTTTGACCTGACGATCTTCCCCCTTCCCGCCTTCAGGTTCCTCCTA SRD5A2.1 NM_000348 GTAGGTCTCCTGGCGTTCTGCCAGCTGGCCTGGGGATTCTGAGTGGTGTCTGCTTAGAGTTTACTCCT ACCCTTCCAGGGA STK15.2 NM_003600 CATCTTCCAGGAGGACCACTCTCTGTGGCACCCTGGACTACCTGCCCCCTGAAATGATTGAAGGTCGG A STK15.8 NM_003600 GCCCCCTGAAATGATTGAAGGTCGGATGCATGATGAGAAGGTGGATCTCTGGAGCCTTGGA STK15 intron NM_003600int2 CATTCACATTTATAAACCCACATGGAGGTTGGTCTTGTCGGGAATTCTTTCCGCCTTTACTTTGGATT 2.1 STK15 intron NM_003600int4 GCGAGGAATGAACCCACAGACTCTTTTGCTTTTAGCGGTCTAACAGAGGCTAAGAGTCTAAATCCACT 4.1 GGTTCTCATGC SULT1E1.1 NM_005420 ATGGTGGCTGGTCATCCAAATCCTGGATCCTTTCCAGAGTTTGTGGAGAAATTCATGCAAGGACAGGT TCCTTAT SULT4A1.1 NM_014351 CACCTGCCCTACCGCTTTCTGCCCTCTGACCTCCACAATGGAGACTCCAAGGTCATCTATATGGCTCG CAACCC SURV.2 NM_001168 TGTTTTGATTCCCGGGCTTACCAGGTGAGAAGTGAGGGAGGAAGAAGGCAGTGTCCCTTTTGCTAGAG CTGACAGCTTTG TBP.1 NM_003194 GCCCGAAACGCCGAATATAATCCCAAGCGGTTTGCTGCGGTAATCATGAGGATAAGAGAGCCACG TFRC.3 NM_003234 GCCAACTGCTTTCATTTGTGAGGGATCTGAACCAATACAGAGCAGACATAAAGGAAATGGGCCTGAGT TST.1 NM_003312 GGAGCCGGATGCAGTAGGACTGGACTCGGGCCATATCCGTGGTGCCGTCAACATGCCTTTCATGGACT T UGT1A3.1 NM_019093 GATGCCCTTGTTTGGTGATCAGATGGACAATGCAAAGCGCATGGAGACTAAGGGAGCTGGAGTGACCC T UGT2B7.2 NM_001074 CAATGGCATCTACGAGGCAATCTACCATGGGATCCCTATGGTGGGGATTCCATTGTTTGCCGATCAAC CTG upa.3 NM_002658 GTGGATGTGCCCTGAAGGACAAGCCAGGCGTCTACACGAGAGTCTCACACTTCTTACCCTGGATCCGC AG VDAC1.1 NM_003374 GCTGCGACATGGATTTCGACATTGCTGGGCCTTCCATCCGGGGTGCTCTGGTGCTAGGTTACGAGGGC TGG VDAC2.1 NM_003375 ACCCACGGACAGACTTGCGCGCGTCCAATGTGTATTCCTCCATCATATGCTGACCTTGGCAAAGCT XPC.1 NM_004628 GATACATCGTCTGCGAGGAATTCAAAGACGTGCTCCTGACTGCCTGGGAAAATGAGCAGGCAGTCATT GAAAG Study Methods Gene Expression

For each patient sample included in the study, 50 ng of RNA extracted from a FPET sample was amplified using commercially available RNA amplification kits and protocols (Genisphere). Expression levels of test and reference genes listed in Table 5 were reported as (C_(T)) values from the qRT-PCR assay (TaqMan®). Based on the relative invariability of their measured expression in study samples and on the lack of observed correlation between their measured expression and clinical outcome, CDH1, TBP, EPHX1, SERPINE1 and CD68 were chosen as reference genes. Test gene expression values were normalized relative to the mean of these reference genes. Reference-normalized expression measurements typically range from 0 to 15, where a one unit increase generally reflects a 2-fold increase in RNA quantity.

Main effect Cox proportional hazard models (D. R. Cox (1972) Regression Models and Life-Tables (with discussion). J Royal Statistical Soc. B, 34:187-220) were utilized to compare the additional contribution of gene expression beyond standard clinical prognostics variables, including age, clinical tumor size, and tumor grade. A test for comparing the reduced model, excluding the gene expression variable, versus the competing full model including the gene variable of interest, called the likelihood ratio test (Ronald Fisher (1922) “On the Mathematical Foundations of Theoretical Statistics”, Phil. Trans. Royal Soc., series A, 222:326, 1922; Leonard Savage (1962), The Foundations of Statistical Inference (1962)) was utilized to identify statistically significant prognostic genes.

Study Results

Using the methods described above, 34 genes were identified, for which the expression level was found to be significantly correlated with DRFS (p<0.1). The genes are shown in Table 8 together with Hazard Ratio and p-values. Results utilizing two distinct probe primer sets designed to measure distinct expression products of the PGR gene are shown. The PR.12 probe primer set is targeted specifically toward PGR-B mRNA, which gives rise to a longer translation product than does PGR-A mRNA. PR.6 recognizes both PGR-A and PGR-B. Measurement using PR.12 resulted in a lower Hazard Ratio than did PR.6, indicating that PGR-B may be the more powerful predictor of clinical outcome. TABLE 8 Gene LR Official Amplicon Name Hazard HR HR P- Symbol (Results) Ratio 95% LCL 95% UCL Value BCL2 Bcl2 intron 1 0.64 0.52 0.80 0.0002 50kb.1 GSTM2 GSTM2 gene.4 0.64 0.49 0.83 0.0003 GSTM3 GSTM3.6 0.57 0.42 0.78 0.0003 SCUBE2 CEGP1.6 0.76 0.65 0.88 0.0003 BCL2 Bcl2-beta.1 0.62 0.47 0.81 0.0007 GSTM1 GSTM1.1 0.71 0.58 0.86 0.0009 PGR PR.6 0.81 0.71 0.92 0.0019 MVP MVP.1 0.44 0.26 0.74 0.0026 GSTM4 GSTM4.1 0.68 0.53 0.87 0.0044 PGR PR.12 0.64 0.46 0.90 0.0067 BIRC5 SURV.2 1.41 1.08 1.82 0.0091 NAT1 NAT1.1 0.85 0.74 0.97 0.0161 CRYZ CRYZ.1 0.60 0.38 0.93 0.0263 GPX1 GPX1.2 0.41 0.19 0.88 0.0263 MKI67 Ki-67.2 1.41 1.02 1.93 0.0270 PRAME PRAME.3 1.17 1.02 1.33 0.0270 PPIH CYP.1 0.58 0.36 0.92 0.0283 CYP17A1 CYP17A1.1 0.69 0.49 0.99 0.0323 IL17RB IL17RB.2 0.81 0.68 0.98 0.0334 CAT CAT.1 0.63 0.41 0.96 0.0400 CYP4Z1 CYP4Z1.1 0.86 0.75 1.00 0.0416 ESR1 EstR1.1 0.87 0.77 0.99 0.0418 GPX2 GPX2.2 0.68 0.48 0.98 0.0419 PRDX3 PRDX3.1 0.55 0.31 0.98 0.0454 STK6 STK15.2 1.63 0.99 2.69 0.0475 GSTM5 GSTM5.2 0.77 0.58 1.02 0.0493 SC5DL SC5DL.1 0.65 0.42 1.00 0.0520 CTSL2 CTSL2.10 1.22 1.00 1.49 0.0620 VDAC1 VDAC1.1 1.89 0.96 3.72 0.0689 PLAU upa.3 0.66 0.43 1.03 0.0716 TFRC TFRC.3 1.49 0.97 2.30 0.0759 NQO1 NQO1.1 1.42 0.95 2.13 0.0803 GSTP1 GSTp.3 0.72 0.49 1.05 0.0840 ATP5A1 ATP5A1.1 0.56 0.30 1.07 0.0850 GUSB GUS.1 0.67 0.42 1.08 0.0861

Two genes from the glutathione peroxidase family, GPX1 and GPX2, were positive prognosticators. GPX1 gave a very strong positive Cox value (H.R.=0.41, p=0.0263) and GPX2 was also strongly positive (H.R.=0.68, p=0.0419). GPX1 encodes a selenium-dependent glutathione peroxidase that functions in the detoxification of hydrogen peroxide, and is one of the most important antioxidant enzymes in humans. GPX1 overexpression delays cell growth and protects from GSH and H₂O₂ toxicity. Interestingly, these biological activities are similar to BCL2, another strong positive prognostic indicator in breast. GPX2 also encodes a selenium-dependent glutathione peroxidase and is one of two isoenzymes responsible for the majority of the glutathione-dependent hydrogen peroxide-reducing activity in the epithelium of the gastrointestinal tract. Studies in knockout mice indicate that mRNA expression levels respond to luminal microflora, suggesting a role of GPX2 in preventing inflammation in the GI tract.

Another strong positive Cox value was found with peroxiredoxin 3, (PRDX3; H.R.=0.55, p=0.0454). This gene encodes a protein with antioxidant function and is localized in the mitochondrion. PRDX3 is a member of a gene family that is responsible for regulation of cellular proliferation, differentiation, and antioxidant functions.

The strong positive prognostic effect of CRYZ (H.R.=0.60, p=0.0263) is also consistent with its function as an antioxidant. CRYZ encodes the major detoxifying enzyme quinone reductase (QR) [NAD(P)H:quinone oxidoreductase]. It is hypothesized that QR inhibits estrogen-induced DNA damage by detoxification of reactive catecholestrogens. CRYZ is transcriptionally activated by anti-estrogen liganded ERβ. Up-regulation of QR, either by overexpression or induction by tamoxifen, can protect breast cells against oxidative DNA damage caused by estrogen metabolites, representing a possible novel mechanism of tamoxifen prevention against breast cancer. (See Table 9 Univariate Cox PH regression analysis. Assays are ordered by p-value, with p-values≦0.05 considered significant. Specimens from 125 breast cancer patients were assayed.) TABLE 9 Hazard HR HR Univariate Analysis Ratio 95% LCL 95% UCL P-Value CRYZ.1 0.60 0.38 0.93 0.0263 CYP1B1.3 0.81 0.55 1.19 0.2852 UGT2B7.2 1.07 0.94 1.22 0.3763 SULT1E1.1 1.08 0.91 1.28 0.3862 COMT.1 0.87 0.42 1.81 0.711 SULT4A1.1 1.01 0.82 1.25 0.9427 CYP1A1.2 1.01 0.82 1.23 0.949 UGT1A3.1 1.00 0.78 1.27 0.974

The cytochrome P450 proteins are monooxygenases which catalyze many reactions involved in drug metabolism and synthesis of cholesterol, steroids and other lipids. Two of the five cytochrome P450 superfamily members tested were also significant indicators of positive prognosis. CYP17A1 (H.R.=0.69, p=0.0323) localizes to the endoplasmic reticulum. It has both 17alpha-hydroxylase and 17,20-lyase activities and is a key enzyme in the steroidogenic pathway that produces progestins, mineralocorticoids, glucocorticoids, androgens, and estrogens. The recently discovered CYP4Z1 (H.R.=0.86, p=0.0416), also an endoplasmic reticulum integral membrane protein, is restricted to expression in breast and showed a clear over-expression in 52% of breast cancer samples in one study.

The antioxidant protein catalase (CAT) is located at the peroxisome and scavenges H₂O₂. Consistent with its function was the finding that CAT expression correlated with positive prognosis (H.R.=0.63, p=0.040).

The sterol-C5-desaturase like gene (SC5DL) encodes an enzyme that is involved in cholesterol biosynthesis. Expression of SC5DL is downregulated in human ovarian carcinomas in vivo during Taxol(R) (paclitaxel) treatment. In our study, increased expression of SC5DL was a positive prognostic indicator (H.R.=0.65, p=0.052).

NAT1, a xenobiotic-metabolizing enzyme, is an ERα-responsive gene in human breast cancer and has been suggested as a candidate molecular predictor of antiestrogen responsiveness. In a 97 ERalpha-positive breast tumor study, relapse-free survival was longer among patients with NAT1-overexpressing tumors (P =0.000052), and retained prognostic significance in Cox multivariate regression analysis (P=0.0013). In our current study, we show that NAT1 maintains a positive prognostic significance in a univariate Cox model (H.R.=0.85, p=0.0161) NAT1 also shows a strong expression correlation with ER (R=0.67), consistent with it being an ERα responsive gene.

The glutathione S-transferase pi gene (GSTP1) is a polymorphic gene encoding active, functionally different GSTP1 variant proteins that are thought to function in xenobiotic metabolism and play a role in susceptibility to cancer. GSTp was a positive prognostic indicator in our study (H.R.=0.72, p=0.084).

One skilled in the art will recognize numerous methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention. Indeed, the present invention is in no way limited to the methods and materials described. While the present invention has been described with reference to what are considered to be the specific embodiments, it is to be understood that the invention is not limited to such embodiments. To the contrary, the invention is intended to cover various modifications and equivalents included within the spirit and scope of the appended claims. For example, while the disclosure is illustrated by identifying genes and groups of genes useful in determining prognosis for patients diagnosed with invasive breast cancer, similar methods in determining prognosis for patients diagnosed with cancer of other cell types, including prostate and ovarian cancer. 

1. A method of predicting clinical outcome for a subject diagnosed with cancer, comprising determining evidence of the expression level of one or more predictive RNA transcripts listed in Table 8, or their expression products, in a biological sample comprising cancer cells obtained from said subject, wherein evidence of increased expression of one or more of the genes listed in Table 8, or the corresponding expression product, indicates a decreased likelihood of a positive clinical outcome.
 2. The method of claim 1 wherein said subject is a human patient.
 3. The method of claim 1 wherein evidence of said expression level is obtained by a method of gene expression profiling.
 4. The method of claim 3 wherein said method is a PCR-based method.
 5. The method of claim 4 wherein said expression levels are normalized relative to the expression levels of one or more reference genes, or their expression products.
 6. The method of claim 2 wherein said clinical outcome is expressed in terms of Recurrence-Free Interval (RFI), Overall Survival (OS), Disease-Free Survival (DFS), or Distant Recurrence-Free Interval (DRFI).
 7. The method of claim 2 wherein said cancer is selected from the group consisting of breast cancer or ovarian cancer.
 8. The method of claim 7 wherein said cancer is breast cancer.
 9. The method of claim 2 comprising determining evidence of the expression levels of at least two of said genes, or their expression products.
 10. The method of claim 2 comprising determining evidence of the expression levels of at least three of said genes, or their expression products.
 11. The method of claim 2 comprising determining evidence of the expression levels of at least four of said genes, or their expression products.
 12. The method of claim 2 comprising determining evidence of the expression levels of at least five of said genes, or their expression products.
 13. The method of claim 2 further comprising the step of creating a report summarizing said prediction.
 14. A method of predicting the duration of Recurrence-Free Interval (RFI) in a subject diagnosed with breast cancer, comprising determining the expression level of one or more predictive RNA transcripts listed in Table 8 or their expression products, in a biological sample comprising cancer cells obtained from said subject, wherein evidence of increased expression of one or more of the genes-listed in Table 8, or the corresponding expression product, indicates that said RFI is predicted to be shorter.
 15. The method of claim 14 wherein said subject is a human patient.
 16. The method of claim 14 wherein evidence of said expression level is obtained by a method of gene expression profiling.
 17. The method of claim 16 wherein said method is a PCR-based method.
 18. The method of claim 17 wherein, if said RFI is predicted to be shorter, said patient is subjected to further therapy following surgical removal of the cancer.
 19. The method of claim 18 wherein said further therapy is chemotherapy and/or radiation therapy.
 20. The method of claim 15 further comprising the step of creating a report summarizing said prediction.
 21. The method of claim 15 comprising determining evidence of the expression levels of at least two of said genes, or their expression products.
 22. The method of claim 15 comprising determining evidence of the expression levels of at least three of said genes, or their expression products.
 23. The method of claim 15 comprising determining evidence of the expression levels of at least four of said genes, or their expression products.
 24. The method of claim 15 comprising determining evidence of the expression levels of at least five of said genes, or their expression products.
 25. A method of predicting clinical outcome for a subject diagnosed with cancer, comprising determining evidence of the expression level of one or more predictive RNA transcripts of one or more genes selected from the group consisting of CAT, CRYZ, CYP4Z1, CYP17A1, GPX1, GPX2, GSTM1, GSTM2, GSTM3, GSTM4, GSTM5, GSTP1, NQO1, PRDX3, and SC5DL or their expression products, in a biological sample comprising cancer cells obtained from said subject, wherein evidence of increased expression of one or more of the genes, or the corresponding expression product, indicates a decreased likelihood of a positive clinical outcome.
 26. The method of claim 25 comprising determining the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of GSTM1, GSTM2, GSTM3, GSTM4, GSTM5 and GSTP1.
 27. The method of claim 25 comprising determining the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of GSTM2 and GSTM4.
 28. The method of claim 25 comprising determining the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of GSTM1 and GSTM3.
 29. The method of claim 25 comprising determining the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of CAT, PRDX3, GPX1, and GPX2.
 30. The method of claim 29 comprising determining the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of PRDX3, GPX1 and GPX2.
 31. The method of claim 30 comprising determining the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of GPX1 and GPX2.
 32. The method of claim 25 comprising determining the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of CRYZ and NQO1.
 33. The method of claim 25 comprising determining the expression level of one or more predictive RNA transcripts or their expression products of CYP17A1.
 34. The method of claim 25 comprising determining the expression level of one or more predictive RNA transcripts or their expression products of one or more genes selected from the group consisting of SC5DL and CYP4Z1.
 35. A method for preparing a personalized genomics profile for a patient comprising the steps of (a) subjecting RNA extracted from a tissue obtained from the patient to gene expression analysis; (b) determining the expression level in the tissue of one or more genes selected from the gene set listed in Table 8, wherein the expression level is normalized against a control gene or genes and optionally is compared to the amount found in a cancer reference set and (c) creating a report summarizing the data obtained by said gene expression analysis.
 36. A method for amplification of a gene listed in Table 8 by polymerase chain reaction (PCR) comprising performing said pcr by using amplicons listed in Table 7 and a primer-probe set listed in Table
 6. 37. A PCR primer-probe set listed in Table
 6. 38. A PCR amplicon listed in Table
 7. 